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-0,0 +1,69 @@ +#!/usr/bin/env jupyter +# --- +# jupyter: +# jupytext: +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.15.2 +# kernelspec: +# display_name: Python 3 +# language: python +# name: python3 +# --- +# %% [Markdown] +""" +Find correlations between CD44 and HAS2. Question inspired by Prof. Chonghui Chen from Baylor College of Medicine during ASCB2 2023. +""" + +import matplotlib.pyplot as plt +import seaborn as sns +from broad_babel.query import run_query +from utils import load_path, get_figs_dir + +figs_dir = get_figs_dir() +df = load_path("../../profiles/harmonized_no_sphering_profiles.parquet") + +genes_of_interest = ("CD44", "HAS2") +cd44_crispr, *cd44_orf = run_query( + query="CD44", input_column="standard_key", output_column="JCP2022" +)[0] + +gene_matches = {pert: dict() for pert in genes_of_interest} +for gene in genes_of_interest: + crispr, *orf = list( + map( + lambda x: x[0], + run_query(query=gene, input_column="standard_key", output_column="JCP2022"), + ) + ) + gene_matches[gene]["crispr"] = crispr + if len(orf): + gene_matches[gene]["orf"] = orf[0] + + +# %% Calculate the correlations between has2 and cd44 based on CRISPR data + +feat_cols = [x for x in df.columns if not x.startswith("Metadata")] +data_only = df.loc[ + df["Metadata_JCP2022"].isin([x["crispr"] for x in gene_matches.values()]) +] +data_only.set_index( + data_only["Metadata_JCP2022"].map( + {v["crispr"]: k for k, v in gene_matches.items()} + ), + inplace=True, +) +for method in ("pearson", "kendall", "spearman"): + candidates_corr = ( + data_only[feat_cols].T.corr(method=method).sort_index().sort_index(axis=1) + ) + sns.heatmap(candidates_corr, robust=True) + plt.tight_layout() + plt.savefig( + figs_dir + / f"correlations_{method}_harmony_{'_'.join(genes_of_interest).lower() }.png", + dpi=300, + ) + plt.close() diff --git a/workspace/analysis/CD44_HAS2/utils.py b/workspace/analysis/CD44_HAS2/utils.py new file mode 100644 index 0000000..e76d40d --- /dev/null +++ b/workspace/analysis/CD44_HAS2/utils.py @@ -0,0 +1,14 @@ +#!/usr/bin/env jupyter + +import pandas as pd +from pathlib import Path + + +def load_path(path: str or Path): + return pd.read_parquet(Path(path)) + + +def get_figs_dir(): + figs_dir = Path("../../figs") + figs_dir.mkdir(exist_ok=True) + return figs_dir diff --git a/workspace/analysis/MYT1_RNF41/1_correlations.py b/workspace/analysis/MYT1_RNF41/1_correlations.py new file mode 100644 index 0000000..69dd702 --- /dev/null +++ b/workspace/analysis/MYT1_RNF41/1_correlations.py @@ -0,0 +1,134 @@ +#!/usr/bin/env jupyter +#!/usr/bin/env jupyter +# --- +# jupyter: +# jupytext: +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.15.2 +# kernelspec: +# display_name: Python 3 +# language: python +# name: python3 +# --- +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import seaborn as sns +from broad_babel.query import run_query +from matplotlib.ticker import MaxNLocator + +dataset_filepath = Path("../profiles/harmonized_no_sphering_profiles.parquet") +figs_dir = Path("../figs") +figs_dir.mkdir(exist_ok=True) + +df = pd.read_parquet(dataset_filepath) + +myt1_crispr, orf = [ + x[0] + for x in run_query( + query="MYT1", input_column="standard_key", output_column="JCP2022" + ) +] +rnf41_crispr = run_query( + query="RNF41", input_column="standard_key", output_column="JCP2022" +)[0][0] + + +# %% Calculate the correlations of myt1 and rnf41 +# They were reported to interact by collaborators, and Ardigen found them to correlate + +feat_cols = [x for x in df.columns if not x.startswith("Metadata")] +data_only = df.loc[df["Metadata_JCP2022"].isin([myt1_crispr, rnf41_crispr])] +data_only.set_index( + data_only["Metadata_JCP2022"].map({rnf41_crispr: "rnf41", myt1_crispr: "myt1"}), + inplace=True, +) +for method in ("pearson", "kendall", "spearman"): + candidates_corr = ( + data_only[feat_cols].T.corr(method=method).sort_index().sort_index(axis=1) + ) + sns.heatmap(candidates_corr) + plt.tight_layout() + plt.savefig(figs_dir / f"correlations_{method}_harmony_myt1_rnf42.png", dpi=300) + plt.close() + +# %% + +averaged = df.groupby("Metadata_JCP2022")[feat_cols].mean() +corr = averaged.T.corr() + +myt1_correlated = corr.loc[myt1_crispr] + +# %% Plot the correlations and thresholds +sns.set_style("white") +sns.set_context("talk") +ax = sns.lineplot(data=myt1_correlated.sort_values()) +ax.set(xticklabels=[]) +ax.spines[["right", "top"]].set_visible(False), +plt.title("Gene of interest: MYT1") +plt.xlabel("Genes ranked by correlation") +ax.set_ylabel("Pearson correlation") +ax.vlines( + [100, len(myt1_correlated) - 100], + ymin=-1, + ymax=1, + color="xkcd:dull red", + linestyles="dashed", +) + + +def arrow(ax, x, y, direction): + return ax.arrow( + x, + y, + 800 * direction, + 0, + length_includes_head=True, + color="black", + head_width=0.1, + head_length=300, + ) + + +arrow(ax, 6700, 0.92, 1) +arrow(ax, 1300, -0.87, -1) +ax.xaxis.set_major_locator(MaxNLocator(16)) +plt.tight_layout() +plt.savefig(figs_dir / "scatter_ranked.png", dpi=300) +plt.close() + +# %% + + +best_matches = [*myt1_correlated.index[:100], *myt1_correlated.index[-100:]] + +best_matches_df = averaged.loc[[myt1_crispr, *best_matches]] + +matches_std = best_matches_df.std().sort_values(ascending=False) + +# %% + +sns.set_style("white") +sns.set_context("talk") +# ax = sns.lineplot(data=matches_std.iloc[:300], color="xkcd:sky blue") +ax = sns.lineplot(data=matches_std.iloc[:300], color="xkcd:sky blue") +ax.set(xticklabels=[]) +ax.spines[["right", "top"]].set_visible(False), +plt.title("Feature variability of matched genes") +plt.xlabel("Features ranked by variability") +ax.set_ylabel("Normalised variability") + +ax.vlines( + [30], + ymin=matches_std.min(), + ymax=matches_std.max(), + color="xkcd:dull red", + linestyles="dashed", +) +plt.savefig(figs_dir / "ranked_features.png", dpi=300) +plt.close() From e96093bdfab5032f9953267205e409c35807bc39 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Al=C3=A1n=20F=2E=20Mu=C3=B1oz?= Date: Tue, 12 Dec 2023 15:07:44 -0500 Subject: [PATCH 03/38] change(MYT1_RNF41): replace pearson->cosine dist --- .../analysis/MYT1_RNF41/1_correlations.py | 136 ++++++------------ 1 file changed, 44 insertions(+), 92 deletions(-) diff --git a/workspace/analysis/MYT1_RNF41/1_correlations.py b/workspace/analysis/MYT1_RNF41/1_correlations.py index 69dd702..78f4803 100644 --- a/workspace/analysis/MYT1_RNF41/1_correlations.py +++ b/workspace/analysis/MYT1_RNF41/1_correlations.py @@ -13,6 +13,7 @@ # language: python # name: python3 # --- +from itertools import product from pathlib import Path import matplotlib.pyplot as plt @@ -20,115 +21,66 @@ import pandas as pd import seaborn as sns from broad_babel.query import run_query -from matplotlib.ticker import MaxNLocator +from copairs.compute import pairwise_cosine -dataset_filepath = Path("../profiles/harmonized_no_sphering_profiles.parquet") +dataset_filepath = ( + Path("..") / ".." / "profiles" / "harmonized_no_sphering_profiles.parquet" +) figs_dir = Path("../figs") figs_dir.mkdir(exist_ok=True) df = pd.read_parquet(dataset_filepath) -myt1_crispr, orf = [ - x[0] - for x in run_query( - query="MYT1", input_column="standard_key", output_column="JCP2022" +genes_of_interest = ("RNF41", "MYT1") +# cd44_crispr = run_query(query="CD44", input_column="standard_key", output_column="*") + +gene_matches = {pert: dict() for pert in genes_of_interest} +for gene in genes_of_interest: + results = list( + map( + lambda x: x, + run_query(query=gene, input_column="standard_key", output_column="*"), + ) ) -] -rnf41_crispr = run_query( - query="RNF41", input_column="standard_key", output_column="JCP2022" -)[0][0] + for pert_type in ("crispr", "orf"): + result_array = [x[1] for x in results if x[0] == pert_type] + if len(result_array): + gene_matches[gene][pert_type] = result_array[0] # %% Calculate the correlations of myt1 and rnf41 # They were reported to interact by collaborators, and Ardigen found them to correlate feat_cols = [x for x in df.columns if not x.startswith("Metadata")] -data_only = df.loc[df["Metadata_JCP2022"].isin([myt1_crispr, rnf41_crispr])] -data_only.set_index( - data_only["Metadata_JCP2022"].map({rnf41_crispr: "rnf41", myt1_crispr: "myt1"}), +translator_crispr_jump_gene = {v["crispr"]: k for k, v in gene_matches.items()} +subset = df.loc[df["Metadata_JCP2022"].isin(translator_crispr_jump_gene)] +subset.set_index( + subset["Metadata_JCP2022"].map(translator_crispr_jump_gene), inplace=True, ) -for method in ("pearson", "kendall", "spearman"): - candidates_corr = ( - data_only[feat_cols].T.corr(method=method).sort_index().sort_index(axis=1) - ) - sns.heatmap(candidates_corr) - plt.tight_layout() - plt.savefig(figs_dir / f"correlations_{method}_harmony_myt1_rnf42.png", dpi=300) - plt.close() - -# %% - -averaged = df.groupby("Metadata_JCP2022")[feat_cols].mean() -corr = averaged.T.corr() - -myt1_correlated = corr.loc[myt1_crispr] -# %% Plot the correlations and thresholds -sns.set_style("white") -sns.set_context("talk") -ax = sns.lineplot(data=myt1_correlated.sort_values()) -ax.set(xticklabels=[]) -ax.spines[["right", "top"]].set_visible(False), -plt.title("Gene of interest: MYT1") -plt.xlabel("Genes ranked by correlation") -ax.set_ylabel("Pearson correlation") -ax.vlines( - [100, len(myt1_correlated) - 100], - ymin=-1, - ymax=1, - color="xkcd:dull red", - linestyles="dashed", -) - - -def arrow(ax, x, y, direction): - return ax.arrow( - x, - y, - 800 * direction, - 0, - length_includes_head=True, - color="black", - head_width=0.1, - head_length=300, +data_subset_no_features = subset[feat_cols] +n_samples = len(subset) +all_pair_ixs = np.array(list(product(range(n_samples), range(n_samples)))) +cosine_distance = pairwise_cosine( + data_subset_no_features.values, all_pair_ixs, batch_size=min(200, n_samples) +).reshape((n_samples, n_samples)) +cosine_df = ( + pd.DataFrame( + data=cosine_distance, + index=subset.index, + columns=subset.index, ) - - -arrow(ax, 6700, 0.92, 1) -arrow(ax, 1300, -0.87, -1) -ax.xaxis.set_major_locator(MaxNLocator(16)) + .sort_index(axis=0) + .sort_index(axis=1) +) +sns.heatmap( + cosine_df, + robust=True, +) plt.tight_layout() -plt.savefig(figs_dir / "scatter_ranked.png", dpi=300) -plt.close() - -# %% - - -best_matches = [*myt1_correlated.index[:100], *myt1_correlated.index[-100:]] - -best_matches_df = averaged.loc[[myt1_crispr, *best_matches]] - -matches_std = best_matches_df.std().sort_values(ascending=False) - -# %% - -sns.set_style("white") -sns.set_context("talk") -# ax = sns.lineplot(data=matches_std.iloc[:300], color="xkcd:sky blue") -ax = sns.lineplot(data=matches_std.iloc[:300], color="xkcd:sky blue") -ax.set(xticklabels=[]) -ax.spines[["right", "top"]].set_visible(False), -plt.title("Feature variability of matched genes") -plt.xlabel("Features ranked by variability") -ax.set_ylabel("Normalised variability") - -ax.vlines( - [30], - ymin=matches_std.min(), - ymax=matches_std.max(), - color="xkcd:dull red", - linestyles="dashed", +plt.savefig( + figs_dir / f"heatmap_cosdist_harmony_{'_'.join(genes_of_interest).lower() }.png", + dpi=300, ) -plt.savefig(figs_dir / "ranked_features.png", dpi=300) plt.close() From 6bffbeaa4c3398f1c38be8c97e4e60584c248533 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Al=C3=A1n=20F=2E=20Mu=C3=B1oz?= Date: Tue, 12 Dec 2023 15:39:59 -0500 Subject: [PATCH 04/38] change(MYT1): cover case no crispr --- workspace/analysis/MYT1_RNF41/1_correlations.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/workspace/analysis/MYT1_RNF41/1_correlations.py b/workspace/analysis/MYT1_RNF41/1_correlations.py index 78f4803..8ca9742 100644 --- a/workspace/analysis/MYT1_RNF41/1_correlations.py +++ b/workspace/analysis/MYT1_RNF41/1_correlations.py @@ -52,7 +52,7 @@ # They were reported to interact by collaborators, and Ardigen found them to correlate feat_cols = [x for x in df.columns if not x.startswith("Metadata")] -translator_crispr_jump_gene = {v["crispr"]: k for k, v in gene_matches.items()} +translator_crispr_jump_gene = {v.get("crispr"): k for k, v in gene_matches.items()} subset = df.loc[df["Metadata_JCP2022"].isin(translator_crispr_jump_gene)] subset.set_index( subset["Metadata_JCP2022"].map(translator_crispr_jump_gene), From bce77c982315b42ccbab66cd764983a9d422e8a9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Al=C3=A1n=20F=2E=20Mu=C3=B1oz?= Date: Tue, 12 Dec 2023 15:40:28 -0500 Subject: [PATCH 05/38] change(deps): use copairs v0.3.3 --- poetry.lock | 402 ++++++++++++++++++++++++++++++++----------------- pyproject.toml | 5 +- 2 files changed, 262 insertions(+), 145 deletions(-) diff --git a/poetry.lock b/poetry.lock index 949e70b..dc5df21 100644 --- a/poetry.lock +++ b/poetry.lock @@ -628,6 +628,29 @@ mypy = ["contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.4.1)", "types-Pill test = ["Pillow", "contourpy[test-no-images]", "matplotlib"] test-no-images = ["pytest", "pytest-cov", "wurlitzer"] +[[package]] +name = "copairs" +version = "0.3.3" +description = "Find pairs and compute metrics between them" +optional = false +python-versions = ">=3.8" +files = [] +develop = false + +[package.dependencies] +pandas = "*" +statsmodels = "*" +tqdm = "*" + +[package.extras] +plot = ["plotly"] + +[package.source] +type = "git" +url = "https://github.com/cytomining/copairs" +reference = "v0.3.3" +resolved_reference = "91a282b9b0fa7a239469b3b96e198345ac5a108e" + [[package]] name = "cycler" version = "0.12.1" @@ -883,13 +906,13 @@ files = [ [[package]] name = "importlib-metadata" -version = "6.9.0" +version = "7.0.0" description = "Read metadata from Python packages" optional = false python-versions = ">=3.8" files = [ - {file = "importlib_metadata-6.9.0-py3-none-any.whl", hash = "sha256:1c8dc6839ddc9771412596926f24cb5a553bbd40624ee2c7e55e531542bed3b8"}, - {file = "importlib_metadata-6.9.0.tar.gz", hash = "sha256:e8acb523c335a91822674e149b46c0399ec4d328c4d1f6e49c273da5ff0201b9"}, + {file = "importlib_metadata-7.0.0-py3-none-any.whl", hash = "sha256:d97503976bb81f40a193d41ee6570868479c69d5068651eb039c40d850c59d67"}, + {file = 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-plugins = ["setuptools"] -requirements-deprecated-finder = ["pip-api", "pipreqs"] - [[package]] name = "jedi" version = "0.19.1" @@ -1305,13 +1322,13 @@ jupyter-server = ">=1.1.2" [[package]] name = "jupyter-server" -version = "2.11.1" +version = "2.12.1" description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications." optional = false python-versions = ">=3.8" files = [ - {file = "jupyter_server-2.11.1-py3-none-any.whl", hash = "sha256:4b3a16e3ed16fd202588890f10b8ca589bd3e29405d128beb95935f059441373"}, - {file = "jupyter_server-2.11.1.tar.gz", hash = "sha256:fe80bab96493acf5f7d6cd9a1575af8fbd253dc2591aa4d015131a1e03b5799a"}, + {file = "jupyter_server-2.12.1-py3-none-any.whl", hash = "sha256:fd030dd7be1ca572e4598203f718df6630c12bd28a599d7f1791c4d7938e1010"}, + {file = "jupyter_server-2.12.1.tar.gz", hash = "sha256:dc77b7dcc5fc0547acba2b2844f01798008667201eea27c6319ff9257d700a6d"}, ] [package.dependencies] @@ -1341,13 +1358,13 @@ test = ["flaky", 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scikit-learn = "^1.3.0" scipy = ">=1.8.1" -# kneed = "^0.8.5" matplotlib = "^3.7.3" seaborn = "^0.12.2" broad-babel = "^0.1.4" -# pycytominer = "^0.2.0" -# copairs = {git = "https://github.com/cytomining/copairs", tag = "v0.3.0"} +copairs = {git = "https://github.com/cytomining/copairs", tag = "v0.3.3"} numpy = "*" fastcluster = "^1.2.6" pandas = "*" @@ -30,6 +28,7 @@ jupytext = "^1.15.2" ipdb = "^0.13.13" isort = "^5.12.0" black = "^23.7.0" +pyflakes = "^3.1.0" [build-system] requires = ["poetry-core"] From 50313053fe384ac58c24eb275250cee6b45e82e4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Al=C3=A1n=20F=2E=20Mu=C3=B1oz?= Date: Tue, 12 Dec 2023 15:40:52 -0500 Subject: [PATCH 06/38] change(CD44): Homogeneise processing notebook --- .../analysis/CD44_HAS2/1_correlations.py | 77 ++++++++++++------- 1 file changed, 48 insertions(+), 29 deletions(-) diff --git a/workspace/analysis/CD44_HAS2/1_correlations.py b/workspace/analysis/CD44_HAS2/1_correlations.py index fa2faf2..f651b96 100644 --- a/workspace/analysis/CD44_HAS2/1_correlations.py +++ b/workspace/analysis/CD44_HAS2/1_correlations.py @@ -17,53 +17,72 @@ Find correlations between CD44 and HAS2. Question inspired by Prof. Chonghui Chen from Baylor College of Medicine during ASCB2 2023. """ +from itertools import product +from pathlib import Path + import matplotlib.pyplot as plt +import numpy as np +import pandas as pd import seaborn as sns from broad_babel.query import run_query -from utils import load_path, get_figs_dir +from copairs.compute import pairwise_cosine + +from utils import get_figs_dir, load_path figs_dir = get_figs_dir() df = load_path("../../profiles/harmonized_no_sphering_profiles.parquet") genes_of_interest = ("CD44", "HAS2") -cd44_crispr, *cd44_orf = run_query( - query="CD44", input_column="standard_key", output_column="JCP2022" -)[0] +cd44_crispr = run_query(query="CD44", input_column="standard_key", output_column="*") gene_matches = {pert: dict() for pert in genes_of_interest} for gene in genes_of_interest: - crispr, *orf = list( + results = list( map( - lambda x: x[0], - run_query(query=gene, input_column="standard_key", output_column="JCP2022"), + lambda x: x, + run_query(query=gene, input_column="standard_key", output_column="*"), ) ) - gene_matches[gene]["crispr"] = crispr - if len(orf): - gene_matches[gene]["orf"] = orf[0] - + for pert_type in ("crispr", "orf"): + result_array = [x[1] for x in results if x[0] == pert_type] + if len(result_array): + gene_matches[gene][pert_type] = result_array[0] # %% Calculate the correlations between has2 and cd44 based on CRISPR data + feat_cols = [x for x in df.columns if not x.startswith("Metadata")] -data_only = df.loc[ - df["Metadata_JCP2022"].isin([x["crispr"] for x in gene_matches.values()]) -] -data_only.set_index( - data_only["Metadata_JCP2022"].map( - {v["crispr"]: k for k, v in gene_matches.items()} - ), +translator_crispr_jump_gene = {v.get("crispr"): k for k, v in gene_matches.items()} +subset = df.loc[df["Metadata_JCP2022"].isin(translator_crispr_jump_gene)] +subset.set_index( + subset["Metadata_JCP2022"].map(translator_crispr_jump_gene), inplace=True, ) -for method in ("pearson", "kendall", "spearman"): - candidates_corr = ( - data_only[feat_cols].T.corr(method=method).sort_index().sort_index(axis=1) - ) - sns.heatmap(candidates_corr, robust=True) - plt.tight_layout() - plt.savefig( - figs_dir - / f"correlations_{method}_harmony_{'_'.join(genes_of_interest).lower() }.png", - dpi=300, + +data_subset_no_features = subset[feat_cols] +n_samples = len(subset) +all_pair_ixs = np.array(list(product(range(n_samples), range(n_samples)))) +cosine_distance = pairwise_cosine( + data_subset_no_features.values, all_pair_ixs, batch_size=min(200, n_samples) +).reshape((n_samples, n_samples)) +cosine_df = ( + pd.DataFrame( + data=cosine_distance, + index=subset.index, + columns=subset.index, ) - plt.close() + .sort_index(axis=0) + .sort_index(axis=1) +) +sns.heatmap( + cosine_df, + robust=True, +) +plt.tight_layout() +plt.savefig( + figs_dir / f"heatmap_cosdist_harmony_{'_'.join(genes_of_interest).lower() }.png", + dpi=300, +) +plt.close() + +# %% TODO Once acquired, use ORF data to have a look at these profiles. 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["python-socks", "wsaccel"] test = ["websockets"] +[[package]] +name = "wheel" +version = "0.42.0" +description = "A built-package format for Python" +optional = false +python-versions = ">=3.7" +files = [ + {file = "wheel-0.42.0-py3-none-any.whl", hash = "sha256:177f9c9b0d45c47873b619f5b650346d632cdc35fb5e4d25058e09c9e581433d"}, + {file = "wheel-0.42.0.tar.gz", hash = "sha256:c45be39f7882c9d34243236f2d63cbd58039e360f85d0913425fbd7ceea617a8"}, +] + +[package.extras] +test = ["pytest (>=6.0.0)", "setuptools (>=65)"] + [[package]] name = "widgetsnbextension" version = "4.0.9" @@ -3375,5 +3932,5 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p [metadata] lock-version = "2.0" -python-versions = ">=3.8,<4.0" -content-hash = "73dbdfa7a6a88113769e50692bae341db87e980591697bed9bb15799f21980d6" +python-versions = ">=3.9,<4.0" +content-hash = "651ffcc66e1171f50dfe30bd4dcb519c3bdc1e515fdc09907ddbdc6dfd49318a" diff --git a/pyproject.toml b/pyproject.toml index f1da91f..ee1bffb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,7 +6,7 @@ authors = ["Alan"] readme = "README.md" [tool.poetry.dependencies] -python = ">=3.8,<4.0" +python = ">=3.9,<4.0" PyYAML = "5.3.1" # plotly = "4.14.3" # dvc-s3 = "^2.23.0" @@ -15,12 +15,14 @@ scikit-learn = "^1.3.0" scipy = ">=1.8.1" matplotlib = "^3.7.3" seaborn = "^0.12.2" -broad-babel = "^0.1.4" -copairs = {git = "https://github.com/cytomining/copairs", tag = "v0.3.3"} +broad-babel = "^0.1.8" +copairs = {git = "https://github.com/cytomining/copairs.git", rev = "v0.4.0-alpha"} numpy = "*" fastcluster = "^1.2.6" pandas = "*" pyarrow = "^14.0.1" +joblib = "^1.3.2" +torch = "2.0.0" [tool.poetry.group.dev.dependencies] @@ -29,6 +31,7 @@ ipdb = "^0.13.13" isort = "^5.12.0" black = "^23.7.0" pyflakes = "^3.1.0" +ruff-lsp = "^0.0.48" [build-system] requires = ["poetry-core"] From a0364a69a0bc187c0cf0d1e650ab57308df92031 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Al=C3=A1n=20F=2E=20Mu=C3=B1oz?= Date: Tue, 19 Dec 2023 18:27:00 -0500 Subject: [PATCH 08/38] new(distances): add torch-driven cosine calc --- .../analysis/distances/cosine_sim_crispr.py | 43 +++++++++++++++++++ 1 file changed, 43 insertions(+) create mode 100644 workspace/analysis/distances/cosine_sim_crispr.py diff --git a/workspace/analysis/distances/cosine_sim_crispr.py b/workspace/analysis/distances/cosine_sim_crispr.py new file mode 100644 index 0000000..57ac10e --- /dev/null +++ b/workspace/analysis/distances/cosine_sim_crispr.py @@ -0,0 +1,43 @@ +#!/usr/bin/env jupyter + +from itertools import product +from pathlib import Path + +from copairs.compute import pairwise_cosine + +sfrom crispr import split_parquet + +filename = "harmonized_no_sphering_profiles" +dir_path = Path("../../profiles/") +profiles_path = dir_path / f"{filename}.parquet" +dframe_path = dir_path / f"{filename}_pert.parquet" +meta, vals, _ = split_parquet(parquet_path) +n_samples = len(meta) + +iterator = product((range(n_samples), range(n_samples))) +cosine_dist = np.zeros((n_samples, n_samples), dtype=np.float32) + +a = np.random.random_sample((100, 100)) +b = np.random.random_sample((100, 100)) +iterator = list( product(range(100),range(100)) ) + +def pairwise_cosine(x_sample: np.ndarray, y_sample: np.ndarray) -> np.ndarray: + x_norm = x_sample / np.linalg.norm(x_sample, axis=1)[:, np.newaxis] + y_norm = y_sample / np.linalg.norm(y_sample, axis=1)[:, np.newaxis] + c_sim = np.sum(x_norm * y_norm, axis=1) + return c_sim + + +def cosine_dist(loc): + cosine_dist[loc[0], loc[1]] = pairwise_cosine(vals[loc[0]], vals[loc[1]]) + + +cos_sim1 =np.sum((a @ b.T) / (norm(a) * norm(b)), axis=1) +cos_sim2 = pairwise_cosine(a, b) + +%timeit np.sum((a @ b.T) / (norm(a) * norm(b)), axis=1) +%timeit dot(a, b)/(norm(a)*norm(b)) +%timeit best_candidate(a, b) + +p_umap(cosine_dist, iterator) +np.savez("crispr_cos_dist.npz", cosine_dist) From 1040d786c4e04c0340a03b47fef4e0a42c5cab13 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Al=C3=A1n=20F=2E=20Mu=C3=B1oz?= Date: Tue, 19 Dec 2023 22:16:13 -0500 Subject: [PATCH 09/38] new(distances): add cosine_torch w/polars --- workspace/analysis/distances/cosine_torch.py | 79 ++++++++++++++++++++ 1 file changed, 79 insertions(+) create mode 100644 workspace/analysis/distances/cosine_torch.py diff --git a/workspace/analysis/distances/cosine_torch.py b/workspace/analysis/distances/cosine_torch.py new file mode 100644 index 0000000..b3a0040 --- /dev/null +++ b/workspace/analysis/distances/cosine_torch.py @@ -0,0 +1,79 @@ +#!/usr/bin/env jupyter +""" +Calculate cosine similarity of the CRISPR profiles using GPU + +Based off +https://github.com/UKPLab/sentence-transformers/blob/master/sentence_transformers/util.py#L31 +""" +from collections.abc import Iterable +from pathlib import Path + +import numpy as np +import polars as pl +import torch +from torch import Tensor, device + +device = device("cuda" if torch.cuda.is_available() else "cpu") + +filename = "harmonized_no_sphering_profiles" +dir_path = Path("../../profiles/") +profiles_path = dir_path / f"{filename}.parquet" + + +def cos_sim(a: Tensor, b: Tensor) -> Tensor: + """ + Computes the cosine similarity cos_sim(a[i], b[j]) for all i and j. + + :return: Matrix with res[i][j] = cos_sim(a[i], b[j]) + """ + a_norm = torch.nn.functional.normalize(a, p=2, dim=1) + b_norm = torch.nn.functional.normalize(b, p=2, dim=1) + return torch.mm(a_norm, b_norm.transpose(0, 1)) + + +# Load Metadata +df = pl.read_parquet(profiles_path) + +vals = Tensor(df.select(pl.all().exclude("^Metadata.*$")).to_numpy()) + +# Calculate cosine similarty +cosine_sim = cos_sim(tensor, tensor) + +# Save the upper triangle compressed +linear = cosine_sim[np.triu_indices(len(cosine_sim))] +joblib.dump(linear, "cosine_joblib", compress=("lzma", 9)) + +# Sort by correlation +_sorted = cosine_sim.sort(axis=1) + +n_vals_used = 25 + + +def get_first_last_n(tensor: Tensor, n: int): + return tensor[:, [range(n), range(-n - 1, -1)]].reshape(-1, 2 * n) + + +indices, values = [ + get_first_last_n(x, n_vals_used) for x in (_sorted.indices, _sorted.values) +] + +meta = df.select(pl.col("^Metadata_.*$")) +jcp_col = "Metadata_JCP2022" +jcp_df = pl.DataFrame( + { + "JCP2022": np.repeat(meta.select(pl.col(jcp_col)), n_vals_used * 2).astype( + " Date: Wed, 20 Dec 2023 00:06:11 -0500 Subject: [PATCH 10/38] deps(distances): add adbc driver to save db --- poetry.lock | 2 +- pyproject.toml | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/poetry.lock b/poetry.lock index 5d22982..e38d408 100644 --- a/poetry.lock +++ b/poetry.lock @@ -3933,4 +3933,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p [metadata] lock-version = "2.0" python-versions = ">=3.9,<4.0" -content-hash = "651ffcc66e1171f50dfe30bd4dcb519c3bdc1e515fdc09907ddbdc6dfd49318a" +content-hash = "76399110aae846836c4822e3cbdde00800b7b1eb6b3f6ad571243313e778a1a0" diff --git a/pyproject.toml b/pyproject.toml index ee1bffb..968a323 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -23,6 +23,7 @@ pandas = "*" pyarrow = "^14.0.1" joblib = "^1.3.2" torch = "2.0.0" +adbc-driver-sqlite = "^0.8.0" [tool.poetry.group.dev.dependencies] From 382e88e672923d7e8ac493f56035ab0e10ad0454 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Al=C3=A1n=20F=2E=20Mu=C3=B1oz?= Date: Wed, 20 Dec 2023 00:07:01 -0500 Subject: [PATCH 11/38] fix(distances): gen_cosine..db works --- ...torch.py => gen_cosine_crispr_db_torch.py} | 47 +++++++++++++++---- 1 file changed, 39 insertions(+), 8 deletions(-) rename workspace/analysis/distances/{cosine_torch.py => gen_cosine_crispr_db_torch.py} (63%) diff --git a/workspace/analysis/distances/cosine_torch.py b/workspace/analysis/distances/gen_cosine_crispr_db_torch.py similarity index 63% rename from workspace/analysis/distances/cosine_torch.py rename to workspace/analysis/distances/gen_cosine_crispr_db_torch.py index b3a0040..b68fb0d 100644 --- a/workspace/analysis/distances/cosine_torch.py +++ b/workspace/analysis/distances/gen_cosine_crispr_db_torch.py @@ -5,16 +5,33 @@ Based off https://github.com/UKPLab/sentence-transformers/blob/master/sentence_transformers/util.py#L31 """ -from collections.abc import Iterable from pathlib import Path +import joblib import numpy as np import polars as pl import torch +from broad_babel.query import run_query from torch import Tensor, device +from utils import batch_processing, parallel + device = device("cuda" if torch.cuda.is_available() else "cpu") +assert device.type == "cuda", "Not running on GPUs" + + +def get_first_last_n(tensor: Tensor, n: int): + return tensor[:, [range(n), range(-n - 1, -1)]].reshape(-1, 2 * n) + + +@batch_processing +def jcp_to_standard(x): + return run_query(query=x, input_column="JCP2022", output_column="standard_key")[0][ + 0 + ] + + filename = "harmonized_no_sphering_profiles" dir_path = Path("../../profiles/") profiles_path = dir_path / f"{filename}.parquet" @@ -37,11 +54,10 @@ def cos_sim(a: Tensor, b: Tensor) -> Tensor: vals = Tensor(df.select(pl.all().exclude("^Metadata.*$")).to_numpy()) # Calculate cosine similarty -cosine_sim = cos_sim(tensor, tensor) +cosine_sim = cos_sim(vals, vals) # Save the upper triangle compressed -linear = cosine_sim[np.triu_indices(len(cosine_sim))] -joblib.dump(linear, "cosine_joblib", compress=("lzma", 9)) +joblib.dump(cosine_sim[np.triu_indices(len(cosine_sim))], "cosine_joblib.gz") # Sort by correlation _sorted = cosine_sim.sort(axis=1) @@ -49,10 +65,6 @@ def cos_sim(a: Tensor, b: Tensor) -> Tensor: n_vals_used = 25 -def get_first_last_n(tensor: Tensor, n: int): - return tensor[:, [range(n), range(-n - 1, -1)]].reshape(-1, 2 * n) - - indices, values = [ get_first_last_n(x, n_vals_used) for x in (_sorted.indices, _sorted.values) ] @@ -77,3 +89,22 @@ def get_first_last_n(tensor: Tensor, n: int): jcp_df.write_parquet("cosine_similarity_best.parquet") # And then proceed to add gene names (TODO incorporate NCBI ids once they are in babel) +uniq_jcp = jcp_df.select(pl.col("JCP2022")).to_series().unique().to_list() +mapper_values = parallel(uniq_jcp, jcp_to_standard) +mapper = {jcp: std for jcp, std in zip(uniq_jcp, mapper_values)} + +jcp_translated = jcp_df.with_columns( + pl.col("JCP2022").replace(mapper).alias("standard_key"), + pl.col("matched_JCP2022").replace(mapper), +).rename({"matched_JCP2022": "matched_standard_key"}) +final_version = jcp_translated.select(reversed(sorted(jcp_translated.columns))) + +# TODO add differentiating features when compared to their controls + +db_name = "crispr.db" +final_version.write_database( + table_name="babel", + connection=f"sqlite:{db_name}", + if_exists="replace", + engine="adbc", +) From 86fc731248035e14ea2b36b313e1f401bf13b3e0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Al=C3=A1n=20F=2E=20Mu=C3=B1oz?= Date: Wed, 20 Dec 2023 00:44:18 -0500 Subject: [PATCH 12/38] clean(distances): remove benchmarking script --- .../analysis/distances/cosine_sim_crispr.py | 43 ------------------- 1 file changed, 43 deletions(-) delete mode 100644 workspace/analysis/distances/cosine_sim_crispr.py diff --git a/workspace/analysis/distances/cosine_sim_crispr.py b/workspace/analysis/distances/cosine_sim_crispr.py deleted file mode 100644 index 57ac10e..0000000 --- a/workspace/analysis/distances/cosine_sim_crispr.py +++ /dev/null @@ -1,43 +0,0 @@ -#!/usr/bin/env jupyter - -from itertools import product -from pathlib import Path - -from copairs.compute import pairwise_cosine - -sfrom crispr import split_parquet - -filename = "harmonized_no_sphering_profiles" -dir_path = Path("../../profiles/") -profiles_path = dir_path / f"{filename}.parquet" -dframe_path = dir_path / f"{filename}_pert.parquet" -meta, vals, _ = split_parquet(parquet_path) -n_samples = len(meta) - -iterator = product((range(n_samples), range(n_samples))) -cosine_dist = np.zeros((n_samples, n_samples), dtype=np.float32) - -a = np.random.random_sample((100, 100)) -b = np.random.random_sample((100, 100)) -iterator = list( product(range(100),range(100)) ) - -def pairwise_cosine(x_sample: np.ndarray, y_sample: np.ndarray) -> np.ndarray: - x_norm = x_sample / np.linalg.norm(x_sample, axis=1)[:, np.newaxis] - y_norm = y_sample / np.linalg.norm(y_sample, axis=1)[:, np.newaxis] - c_sim = np.sum(x_norm * y_norm, axis=1) - return c_sim - - -def cosine_dist(loc): - cosine_dist[loc[0], loc[1]] = pairwise_cosine(vals[loc[0]], vals[loc[1]]) - - -cos_sim1 =np.sum((a @ b.T) / (norm(a) * norm(b)), axis=1) -cos_sim2 = pairwise_cosine(a, b) - -%timeit np.sum((a @ b.T) / (norm(a) * norm(b)), axis=1) -%timeit dot(a, b)/(norm(a)*norm(b)) -%timeit best_candidate(a, b) - -p_umap(cosine_dist, iterator) -np.savez("crispr_cos_dist.npz", cosine_dist) From 34714edd04c448074801b45f3a7c5ecfa86986a4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Al=C3=A1n=20F=2E=20Mu=C3=B1oz?= Date: Wed, 20 Dec 2023 00:44:42 -0500 Subject: [PATCH 13/38] change(distances): produce parquet zstd db --- .../distances/gen_cosine_crispr_db_torch.py | 13 +++++-------- 1 file changed, 5 insertions(+), 8 deletions(-) diff --git a/workspace/analysis/distances/gen_cosine_crispr_db_torch.py b/workspace/analysis/distances/gen_cosine_crispr_db_torch.py index b68fb0d..dc085f5 100644 --- a/workspace/analysis/distances/gen_cosine_crispr_db_torch.py +++ b/workspace/analysis/distances/gen_cosine_crispr_db_torch.py @@ -97,14 +97,11 @@ def cos_sim(a: Tensor, b: Tensor) -> Tensor: pl.col("JCP2022").replace(mapper).alias("standard_key"), pl.col("matched_JCP2022").replace(mapper), ).rename({"matched_JCP2022": "matched_standard_key"}) -final_version = jcp_translated.select(reversed(sorted(jcp_translated.columns))) +final_version = jcp_translated.select( + reversed(sorted(jcp_translated.columns)) +).with_columns(pl.col("JCP2022").str.replace("JCP2022_", "").cast(pl.Int32)) # TODO add differentiating features when compared to their controls -db_name = "crispr.db" -final_version.write_database( - table_name="babel", - connection=f"sqlite:{db_name}", - if_exists="replace", - engine="adbc", -) + +final_version.write_parquet("crispr.parquet", compression="zstd") From 39e6282a12e241d1ef8611e4e9bc66a33d8c6002 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Al=C3=A1n=20F=2E=20Mu=C3=B1oz?= Date: Wed, 20 Dec 2023 20:56:43 -0500 Subject: [PATCH 14/38] feat(distances): add ardigen example column --- .../distances/gen_cosine_crispr_db_torch.py | 22 ++++++++++++++++--- 1 file changed, 19 insertions(+), 3 deletions(-) diff --git a/workspace/analysis/distances/gen_cosine_crispr_db_torch.py b/workspace/analysis/distances/gen_cosine_crispr_db_torch.py index dc085f5..1887ad4 100644 --- a/workspace/analysis/distances/gen_cosine_crispr_db_torch.py +++ b/workspace/analysis/distances/gen_cosine_crispr_db_torch.py @@ -69,7 +69,21 @@ def cos_sim(a: Tensor, b: Tensor) -> Tensor: get_first_last_n(x, n_vals_used) for x in (_sorted.indices, _sorted.values) ] + meta = df.select(pl.col("^Metadata_.*$")) +url_col = "url" +meta = meta.with_columns( + pl.concat_str( + pl.col("Metadata_Source"), + pl.col("Metadata_Plate"), + pl.col("Metadata_Well"), + separator="/", + ) + .str.replace(r"^", "https://phenaid.ardigen.com/static-jumpcpexplorer/images/") + .str.replace("$", "_1.jpg") + .alias(url_col) +) + jcp_col = "Metadata_JCP2022" jcp_df = pl.DataFrame( { @@ -81,6 +95,7 @@ def cos_sim(a: Tensor, b: Tensor) -> Tensor: .to_numpy()[indices.flatten()] .astype(" Tensor: pl.col("JCP2022").replace(mapper).alias("standard_key"), pl.col("matched_JCP2022").replace(mapper), ).rename({"matched_JCP2022": "matched_standard_key"}) -final_version = jcp_translated.select( - reversed(sorted(jcp_translated.columns)) -).with_columns(pl.col("JCP2022").str.replace("JCP2022_", "").cast(pl.Int32)) +matches_translated = jcp_translated.select(reversed(sorted(jcp_translated.columns))) +# .with_columns(pl.col("JCP2022").str.replace("JCP2022_", "").cast(pl.Int32)) + +# Add a link to one of the Ardigen images # TODO add differentiating features when compared to their controls From bbc1848f08ec18cdf93a4f86c3ba540f87c9b4f1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Al=C3=A1n=20F=2E=20Mu=C3=B1oz?= Date: Wed, 20 Dec 2023 20:57:49 -0500 Subject: [PATCH 15/38] change(distances): reorganise declarations --- .../analysis/distances/gen_cosine_crispr_db_torch.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/workspace/analysis/distances/gen_cosine_crispr_db_torch.py b/workspace/analysis/distances/gen_cosine_crispr_db_torch.py index 1887ad4..eb4e5c5 100644 --- a/workspace/analysis/distances/gen_cosine_crispr_db_torch.py +++ b/workspace/analysis/distances/gen_cosine_crispr_db_torch.py @@ -32,11 +32,6 @@ def jcp_to_standard(x): ] -filename = "harmonized_no_sphering_profiles" -dir_path = Path("../../profiles/") -profiles_path = dir_path / f"{filename}.parquet" - - def cos_sim(a: Tensor, b: Tensor) -> Tensor: """ Computes the cosine similarity cos_sim(a[i], b[j]) for all i and j. @@ -48,6 +43,10 @@ def cos_sim(a: Tensor, b: Tensor) -> Tensor: return torch.mm(a_norm, b_norm.transpose(0, 1)) +filename = "harmonized_no_sphering_profiles" +dir_path = Path("../../profiles/") +profiles_path = dir_path / f"{filename}.parquet" + # Load Metadata df = pl.read_parquet(profiles_path) From e4c7d73bbc3eb7e0a8d2ef7800d272a283b56df3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Al=C3=A1n=20F=2E=20Mu=C3=B1oz?= Date: Wed, 20 Dec 2023 20:58:04 -0500 Subject: [PATCH 16/38] change(distances): outcomment database save --- workspace/analysis/distances/gen_cosine_crispr_db_torch.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/workspace/analysis/distances/gen_cosine_crispr_db_torch.py b/workspace/analysis/distances/gen_cosine_crispr_db_torch.py index eb4e5c5..6d7ab6d 100644 --- a/workspace/analysis/distances/gen_cosine_crispr_db_torch.py +++ b/workspace/analysis/distances/gen_cosine_crispr_db_torch.py @@ -56,7 +56,7 @@ def cos_sim(a: Tensor, b: Tensor) -> Tensor: cosine_sim = cos_sim(vals, vals) # Save the upper triangle compressed -joblib.dump(cosine_sim[np.triu_indices(len(cosine_sim))], "cosine_joblib.gz") +# joblib.dump(cosine_sim[np.triu_indices(len(cosine_sim))], "cosine_joblib.gz") # Sort by correlation _sorted = cosine_sim.sort(axis=1) From 247449b0748f42289766d94b4b3f20d43808eb7d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Al=C3=A1n=20F=2E=20Mu=C3=B1oz?= Date: Wed, 20 Dec 2023 20:58:45 -0500 Subject: [PATCH 17/38] clean(distances): rm unused imoprrt --- workspace/analysis/distances/gen_cosine_crispr_db_torch.py | 1 - 1 file changed, 1 deletion(-) diff --git a/workspace/analysis/distances/gen_cosine_crispr_db_torch.py b/workspace/analysis/distances/gen_cosine_crispr_db_torch.py index 6d7ab6d..0abb52e 100644 --- a/workspace/analysis/distances/gen_cosine_crispr_db_torch.py +++ b/workspace/analysis/distances/gen_cosine_crispr_db_torch.py @@ -7,7 +7,6 @@ """ from pathlib import Path -import joblib import numpy as np import polars as pl import torch From 7c26e04cc0ab1a601100dd2578e0567772e91bf8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Al=C3=A1n=20F=2E=20Mu=C3=B1oz?= Date: Wed, 20 Dec 2023 20:59:34 -0500 Subject: [PATCH 18/38] fix(distances): add commented zenodo autoupload --- .../distances/gen_cosine_crispr_db_torch.py | 36 ++++++++++++++++++- 1 file changed, 35 insertions(+), 1 deletion(-) diff --git a/workspace/analysis/distances/gen_cosine_crispr_db_torch.py b/workspace/analysis/distances/gen_cosine_crispr_db_torch.py index 0abb52e..e039a23 100644 --- a/workspace/analysis/distances/gen_cosine_crispr_db_torch.py +++ b/workspace/analysis/distances/gen_cosine_crispr_db_torch.py @@ -118,4 +118,38 @@ def cos_sim(a: Tensor, b: Tensor) -> Tensor: # TODO add differentiating features when compared to their controls -final_version.write_parquet("crispr.parquet", compression="zstd") +final_output = "crispr.parquet" +matches_translated.write_parquet(final_output, compression="zstd") + + +# Upload to zenodo +# Automated uploads are not working +# https://github.com/zenodo/zenodo/issues/2506 + +""" +from os.path import expanduser +import requests +with open(expanduser("~") + "/.zenodo") as f: + for line in f.readlines(): + name, access_token = line.strip("\n").split(": ") + +params = {"access_token": access_token} +bucket_url = "https://zenodo.org/api/files/9ffb1b5b-1e3c-4618-ad91-36255e87b57e" +headers = {"Content-Type": "application/json"} +with open(final_output, "rb") as fp: + r = requests.post( + f"{bucket_url}?access_token={params['access_token']}", + data=fp, + headers=headers, + params=params, + ) + + +depos_url = "https://zenodo.org/api/deposit/depositions/10416605" +depos=requests.get(depos_url,params=params) +new = requests.post(depos.json()["links"]["newversion"], params=params, headers={"Accept": "application/json" ,**headers}) + +d = r.post(depos_url.json("")) +curl -X POST-H "Accept: application/json" -H "Content-Type: application/json" -H "Authorization: Bearer " https://sandbox.zenodo.org/api/deposit/depositions/165/actions/newversion + +""" From 9a8b9983650f42a2878be706ea18e0f5c895e23d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Al=C3=A1n=20F=2E=20Mu=C3=B1oz?= Date: Wed, 20 Dec 2023 21:55:24 -0500 Subject: [PATCH 19/38] change(distances): incorporate match image example --- .../distances/gen_cosine_crispr_db_torch.py | 119 ++++++++++-------- 1 file changed, 67 insertions(+), 52 deletions(-) diff --git a/workspace/analysis/distances/gen_cosine_crispr_db_torch.py b/workspace/analysis/distances/gen_cosine_crispr_db_torch.py index e039a23..e22426f 100644 --- a/workspace/analysis/distances/gen_cosine_crispr_db_torch.py +++ b/workspace/analysis/distances/gen_cosine_crispr_db_torch.py @@ -1,8 +1,9 @@ #!/usr/bin/env jupyter """ -Calculate cosine similarity of the CRISPR profiles using GPU +Calculate cosine similarity of the CRISPR profiles using GPU, +then wrangle information and produce an explorable data frame. -Based off +The GPU section is based off this function https://github.com/UKPLab/sentence-transformers/blob/master/sentence_transformers/util.py#L31 """ from pathlib import Path @@ -45,61 +46,72 @@ def cos_sim(a: Tensor, b: Tensor) -> Tensor: filename = "harmonized_no_sphering_profiles" dir_path = Path("../../profiles/") profiles_path = dir_path / f"{filename}.parquet" +checkpoint_path = Path("jcp_df.parquet") + +# Set names +jcp_col = "Metadata_JCP2022" # Name of columns in input data frames +url_col = "example_image_standard_key" +match_url_col = "example_image_match" +url_prefix = "https://phenaid.ardigen.com/static-jumpcpexplorer/images/" +url_suffix = "_1.jpg" # Load Metadata df = pl.read_parquet(profiles_path) -vals = Tensor(df.select(pl.all().exclude("^Metadata.*$")).to_numpy()) - -# Calculate cosine similarty -cosine_sim = cos_sim(vals, vals) - -# Save the upper triangle compressed -# joblib.dump(cosine_sim[np.triu_indices(len(cosine_sim))], "cosine_joblib.gz") +if checkpoint_path.exists(): + jcp_df = pl.read_parquet(checkpoint_path) +else: + vals = Tensor(df.select(pl.all().exclude("^Metadata.*$")).to_numpy()) -# Sort by correlation -_sorted = cosine_sim.sort(axis=1) + # Calculate cosine similarty + cosine_sim = cos_sim(vals, vals) -n_vals_used = 25 + # Sort by correlation + _sorted = cosine_sim.sort(axis=1) + n_vals_used = 25 -indices, values = [ - get_first_last_n(x, n_vals_used) for x in (_sorted.indices, _sorted.values) -] + indices, values = [ + get_first_last_n(x, n_vals_used) for x in (_sorted.indices, _sorted.values) + ] + # Add a link to one of the Ardigen images + meta = df.select(pl.col("^Metadata_.*$")) + meta = meta.with_columns( + pl.concat_str( + pl.col("Metadata_Source"), + pl.col("Metadata_Plate"), + pl.col("Metadata_Well"), + separator="/", + ) + .str.replace(r"^", url_prefix) + .str.replace("$", url_suffix) + .alias(url_col) + ) -meta = df.select(pl.col("^Metadata_.*$")) -url_col = "url" -meta = meta.with_columns( - pl.concat_str( - pl.col("Metadata_Source"), - pl.col("Metadata_Plate"), - pl.col("Metadata_Well"), - separator="/", + jcp_df = pl.DataFrame( + { + "JCP2022": np.repeat(meta.select(pl.col(jcp_col)), n_vals_used * 2).astype( + " Tensor: jcp_translated = jcp_df.with_columns( pl.col("JCP2022").replace(mapper).alias("standard_key"), - pl.col("matched_JCP2022").replace(mapper), -).rename({"matched_JCP2022": "matched_standard_key"}) + pl.col("match_JCP2022").replace(mapper), +).rename({"match_JCP2022": "match_standard_key"}) matches_translated = jcp_translated.select(reversed(sorted(jcp_translated.columns))) # .with_columns(pl.col("JCP2022").str.replace("JCP2022_", "").cast(pl.Int32)) -# Add a link to one of the Ardigen images - -# TODO add differentiating features when compared to their controls - final_output = "crispr.parquet" matches_translated.write_parquet(final_output, compression="zstd") @@ -153,3 +161,10 @@ def cos_sim(a: Tensor, b: Tensor) -> Tensor: curl -X POST-H "Accept: application/json" -H "Content-Type: application/json" -H "Authorization: Bearer " https://sandbox.zenodo.org/api/deposit/depositions/165/actions/newversion """ + +""" +Future plans: +- Add example of matched profile +- Find a way to compress URLs for easier visualisation +- TODO add differentiating features when compared to their controls +""" From 608873b3462790f89c34ddd1c76debf6a6cb0a86 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Al=C3=A1n=20F=2E=20Mu=C3=B1oz?= Date: Wed, 3 Jan 2024 00:26:50 -0500 Subject: [PATCH 20/38] change(distances): add jupytext header --- .../distances/gen_cosine_crispr_db_torch.py | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/workspace/analysis/distances/gen_cosine_crispr_db_torch.py b/workspace/analysis/distances/gen_cosine_crispr_db_torch.py index e22426f..727cdde 100644 --- a/workspace/analysis/distances/gen_cosine_crispr_db_torch.py +++ b/workspace/analysis/distances/gen_cosine_crispr_db_torch.py @@ -1,4 +1,18 @@ #!/usr/bin/env jupyter +# --- +# jupyter: +# jupytext: +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.15.2 +# kernelspec: +# display_name: Python 3 +# language: python +# name: python3 +# --- + """ Calculate cosine similarity of the CRISPR profiles using GPU, then wrangle information and produce an explorable data frame. From 5f02a4b99aea3580c7f257b20e7aa6876bfa143e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Al=C3=A1n=20F=2E=20Mu=C3=B1oz?= Date: Wed, 3 Jan 2024 00:27:18 -0500 Subject: [PATCH 21/38] change(distances/...torch.py): group genes by name --- .../distances/gen_cosine_crispr_db_torch.py | 79 ++++++++++--------- 1 file changed, 40 insertions(+), 39 deletions(-) diff --git a/workspace/analysis/distances/gen_cosine_crispr_db_torch.py b/workspace/analysis/distances/gen_cosine_crispr_db_torch.py index 727cdde..ff11641 100644 --- a/workspace/analysis/distances/gen_cosine_crispr_db_torch.py +++ b/workspace/analysis/distances/gen_cosine_crispr_db_torch.py @@ -62,20 +62,44 @@ def cos_sim(a: Tensor, b: Tensor) -> Tensor: profiles_path = dir_path / f"{filename}.parquet" checkpoint_path = Path("jcp_df.parquet") -# Set names +# %% Set names jcp_col = "Metadata_JCP2022" # Name of columns in input data frames -url_col = "example_image_standard_key" +url_col = "Metadata_image" +match_jcp_col = "Match" match_url_col = "example_image_match" url_prefix = "https://phenaid.ardigen.com/static-jumpcpexplorer/images/" -url_suffix = "_1.jpg" +url_suffix = "_{}.jpg" +n_vals_used = 25 -# Load Metadata +# %% Load Metadata df = pl.read_parquet(profiles_path) +# %% add build url from individual wells +df = df.with_columns( + pl.concat_str( + pl.col("Metadata_Source"), + pl.col("Metadata_Plate"), + pl.col("Metadata_Well"), + separator="/", + ) + .str.replace(r"^", url_prefix) + .str.replace("$", url_suffix) + .alias(url_col) + ) if checkpoint_path.exists(): jcp_df = pl.read_parquet(checkpoint_path) else: - vals = Tensor(df.select(pl.all().exclude("^Metadata.*$")).to_numpy()) + grouped = df.group_by("Metadata_JCP2022") + med = grouped.median() + meta = grouped.agg(pl.col("^Metadata_.*$").map_elements(lambda x: cycle(x))) + + urls = grouped.agg(pl.col(url_col).map_elements(lambda x: cycle(x))) + + # grouped.agg(pl.col("^Metadata_.*$").map_elements(lambda x: cycle(x))) + for srs in meta.iter_columns(): + med.replace_column(med.columns.index(srs.name), srs) + + vals = Tensor(med.select(pl.all().exclude("^Metadata.*$")).to_numpy()) # Calculate cosine similarty cosine_sim = cos_sim(vals, vals) @@ -83,49 +107,27 @@ def cos_sim(a: Tensor, b: Tensor) -> Tensor: # Sort by correlation _sorted = cosine_sim.sort(axis=1) - n_vals_used = 25 indices, values = [ get_first_last_n(x, n_vals_used) for x in (_sorted.indices, _sorted.values) ] - # Add a link to one of the Ardigen images - meta = df.select(pl.col("^Metadata_.*$")) - meta = meta.with_columns( - pl.concat_str( - pl.col("Metadata_Source"), - pl.col("Metadata_Plate"), - pl.col("Metadata_Well"), - separator="/", - ) - .str.replace(r"^", url_prefix) - .str.replace("$", url_suffix) - .alias(url_col) - ) + jcp_ids = urls.select(pl.col(jcp_col)).to_series().to_numpy().astype(" Tensor: jcp_translated = jcp_df.with_columns( pl.col("JCP2022").replace(mapper).alias("standard_key"), - pl.col("match_JCP2022").replace(mapper), -).rename({"match_JCP2022": "match_standard_key"}) + pl.col(match_jcp_col).replace(mapper), +) matches_translated = jcp_translated.select(reversed(sorted(jcp_translated.columns))) -# .with_columns(pl.col("JCP2022").str.replace("JCP2022_", "").cast(pl.Int32)) final_output = "crispr.parquet" From 36eaa6ad469f126bc7fdfabbd56ae47299298155 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Al=C3=A1n=20F=2E=20Mu=C3=B1oz?= Date: Wed, 3 Jan 2024 00:29:08 -0500 Subject: [PATCH 22/38] feat(distances/crispr_db): Add json for html --- workspace/analysis/distances/gen_cosine_crispr_db_torch.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/workspace/analysis/distances/gen_cosine_crispr_db_torch.py b/workspace/analysis/distances/gen_cosine_crispr_db_torch.py index ff11641..097c980 100644 --- a/workspace/analysis/distances/gen_cosine_crispr_db_torch.py +++ b/workspace/analysis/distances/gen_cosine_crispr_db_torch.py @@ -67,8 +67,9 @@ def cos_sim(a: Tensor, b: Tensor) -> Tensor: url_col = "Metadata_image" match_jcp_col = "Match" match_url_col = "example_image_match" -url_prefix = "https://phenaid.ardigen.com/static-jumpcpexplorer/images/" -url_suffix = "_{}.jpg" +url_prefix = ("{\"href\": \"https://phenaid.ardigen.com/" + "static-jumpcpexplorer/images/") +url_suffix = "_{}.jpg\", \"label\": \"example\"}" n_vals_used = 25 # %% Load Metadata From fbc1dace03cda1168c4c35c52e6c6649dc28ed97 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Al=C3=A1n=20F=2E=20Mu=C3=B1oz?= Date: Wed, 3 Jan 2024 00:42:57 -0500 Subject: [PATCH 23/38] fix(distances/crispr_db.py): fix links and imports --- workspace/analysis/distances/gen_cosine_crispr_db_torch.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/workspace/analysis/distances/gen_cosine_crispr_db_torch.py b/workspace/analysis/distances/gen_cosine_crispr_db_torch.py index 097c980..48f2f94 100644 --- a/workspace/analysis/distances/gen_cosine_crispr_db_torch.py +++ b/workspace/analysis/distances/gen_cosine_crispr_db_torch.py @@ -20,6 +20,7 @@ The GPU section is based off this function https://github.com/UKPLab/sentence-transformers/blob/master/sentence_transformers/util.py#L31 """ +from itertools import cycle from pathlib import Path import numpy as np @@ -67,9 +68,9 @@ def cos_sim(a: Tensor, b: Tensor) -> Tensor: url_col = "Metadata_image" match_jcp_col = "Match" match_url_col = "example_image_match" -url_prefix = ("{\"href\": \"https://phenaid.ardigen.com/" +url_prefix = ("{{\"href\": \"https://phenaid.ardigen.com/" "static-jumpcpexplorer/images/") -url_suffix = "_{}.jpg\", \"label\": \"example\"}" +url_suffix = "_{}.jpg\", \"label\": \"example\"}}" n_vals_used = 25 # %% Load Metadata From 47248a65b4e3614892c4d152e128714988fd5e14 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Al=C3=A1n=20F=2E=20Mu=C3=B1oz?= Date: Wed, 3 Jan 2024 02:30:15 -0500 Subject: [PATCH 24/38] feat(distances): implement image json formatting --- .../distances/gen_cosine_crispr_db_torch.py | 125 ++++++++++-------- 1 file changed, 67 insertions(+), 58 deletions(-) diff --git a/workspace/analysis/distances/gen_cosine_crispr_db_torch.py b/workspace/analysis/distances/gen_cosine_crispr_db_torch.py index 48f2f94..c1f1b91 100644 --- a/workspace/analysis/distances/gen_cosine_crispr_db_torch.py +++ b/workspace/analysis/distances/gen_cosine_crispr_db_torch.py @@ -65,12 +65,13 @@ def cos_sim(a: Tensor, b: Tensor) -> Tensor: # %% Set names jcp_col = "Metadata_JCP2022" # Name of columns in input data frames -url_col = "Metadata_image" +url_col = "Metadata_image" # Must start with "Metadata" match_jcp_col = "Match" -match_url_col = "example_image_match" -url_prefix = ("{{\"href\": \"https://phenaid.ardigen.com/" - "static-jumpcpexplorer/images/") -url_suffix = "_{}.jpg\", \"label\": \"example\"}}" +match_url_col = "Match Example" +url_template = ( + '"https://phenaid.ardigen.com/static-jumpcpexplorer/' 'images/{}_{{}}.jpg"' +) +img_formatter = '{{"img_src": {}, "href": {}, "width": 200}}' n_vals_used = 25 # %% Load Metadata @@ -78,60 +79,65 @@ def cos_sim(a: Tensor, b: Tensor) -> Tensor: # %% add build url from individual wells df = df.with_columns( - pl.concat_str( - pl.col("Metadata_Source"), - pl.col("Metadata_Plate"), - pl.col("Metadata_Well"), - separator="/", - ) - .str.replace(r"^", url_prefix) - .str.replace("$", url_suffix) - .alias(url_col) - ) -if checkpoint_path.exists(): - jcp_df = pl.read_parquet(checkpoint_path) -else: - grouped = df.group_by("Metadata_JCP2022") - med = grouped.median() - meta = grouped.agg(pl.col("^Metadata_.*$").map_elements(lambda x: cycle(x))) - - urls = grouped.agg(pl.col(url_col).map_elements(lambda x: cycle(x))) - - # grouped.agg(pl.col("^Metadata_.*$").map_elements(lambda x: cycle(x))) - for srs in meta.iter_columns(): - med.replace_column(med.columns.index(srs.name), srs) - - vals = Tensor(med.select(pl.all().exclude("^Metadata.*$")).to_numpy()) - - # Calculate cosine similarty - cosine_sim = cos_sim(vals, vals) - - # Sort by correlation - _sorted = cosine_sim.sort(axis=1) - - - indices, values = [ - get_first_last_n(x, n_vals_used) for x in (_sorted.indices, _sorted.values) - ] - - - jcp_ids = urls.select(pl.col(jcp_col)).to_series().to_numpy().astype(" Tensor: pl.col("JCP2022").replace(mapper).alias("standard_key"), pl.col(match_jcp_col).replace(mapper), ) + + matches_translated = jcp_translated.select(reversed(sorted(jcp_translated.columns))) @@ -147,11 +155,12 @@ def cos_sim(a: Tensor, b: Tensor) -> Tensor: matches_translated.write_parquet(final_output, compression="zstd") -# Upload to zenodo +""" + +# %% Upload to Zenodo # Automated uploads are not working # https://github.com/zenodo/zenodo/issues/2506 -""" from os.path import expanduser import requests with open(expanduser("~") + "/.zenodo") as f: From 664d564132f65885c79654af38d24574f9122a8c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Al=C3=A1n=20F=2E=20Mu=C3=B1oz?= Date: Wed, 3 Jan 2024 02:32:46 -0500 Subject: [PATCH 25/38] refactor(distance): remove unnecessary lambda --- workspace/analysis/distances/gen_cosine_crispr_db_torch.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/workspace/analysis/distances/gen_cosine_crispr_db_torch.py b/workspace/analysis/distances/gen_cosine_crispr_db_torch.py index c1f1b91..022c482 100644 --- a/workspace/analysis/distances/gen_cosine_crispr_db_torch.py +++ b/workspace/analysis/distances/gen_cosine_crispr_db_torch.py @@ -90,9 +90,9 @@ def cos_sim(a: Tensor, b: Tensor) -> Tensor: ) grouped = df.group_by("Metadata_JCP2022") med = grouped.median() -meta = grouped.agg(pl.col("^Metadata_.*$").map_elements(lambda x: cycle(x))) +meta = grouped.agg(pl.col("^Metadata_.*$").map_elements(cycle)) -urls = grouped.agg(pl.col(url_col).map_elements(lambda x: cycle(x))) +urls = grouped.agg(pl.col(url_col).map_elements(cycle)) for srs in meta.iter_columns(): med.replace_column(med.columns.index(srs.name), srs) From 552ebdd0842e37cd649777b2d6881ade098f488f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Al=C3=A1n=20F=2E=20Mu=C3=B1oz?= Date: Thu, 4 Jan 2024 01:57:25 -0500 Subject: [PATCH 26/38] feat(crispr_db): integrate NCBI ids --- poetry.lock | 8 ++-- pyproject.toml | 2 +- .../distances/gen_cosine_crispr_db_torch.py | 38 ++++++++++++++++--- 3 files changed, 38 insertions(+), 10 deletions(-) diff --git a/poetry.lock b/poetry.lock index e38d408..0ce8732 100644 --- a/poetry.lock +++ b/poetry.lock @@ -320,13 +320,13 @@ css = ["tinycss2 (>=1.1.0,<1.3)"] [[package]] name = "broad-babel" -version = "0.1.8" +version = "0.1.9" description = "A translator of Broad and JUMP ids to more conventional names." optional = false python-versions = ">=3.9,<4.0" files = [ - {file = "broad_babel-0.1.8-py3-none-any.whl", hash = "sha256:4172ac158ad9631066d8404d46aa3a4b66617025cdaa5de64c6b35c9a9ed5fa3"}, - {file = "broad_babel-0.1.8.tar.gz", hash = "sha256:7c18e54a78c1b60848912ef48247e7e21ed96cbe9818397598588e11ac116eff"}, + {file = "broad_babel-0.1.9-py3-none-any.whl", hash = "sha256:7d67a871686570f38c0ea554bd84d497b536d01ba4c511b9e65eb912f9bade01"}, + {file = "broad_babel-0.1.9.tar.gz", hash = "sha256:36d6e001eae200648e1356ea4fe386c37828784619e629ee3807702f2f0cd6c6"}, ] [package.dependencies] @@ -3933,4 +3933,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p [metadata] lock-version = "2.0" python-versions = ">=3.9,<4.0" -content-hash = "76399110aae846836c4822e3cbdde00800b7b1eb6b3f6ad571243313e778a1a0" +content-hash = "43394624778f1ade0117648184d3113eb871e09913e47781cc4d52664b2e4381" diff --git a/pyproject.toml b/pyproject.toml index 968a323..49431c1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -15,7 +15,7 @@ scikit-learn = "^1.3.0" scipy = ">=1.8.1" matplotlib = "^3.7.3" seaborn = "^0.12.2" -broad-babel = "^0.1.8" +broad-babel = "^0.1.9" copairs = {git = "https://github.com/cytomining/copairs.git", rev = "v0.4.0-alpha"} numpy = "*" fastcluster = "^1.2.6" diff --git a/workspace/analysis/distances/gen_cosine_crispr_db_torch.py b/workspace/analysis/distances/gen_cosine_crispr_db_torch.py index 022c482..c6b3423 100644 --- a/workspace/analysis/distances/gen_cosine_crispr_db_torch.py +++ b/workspace/analysis/distances/gen_cosine_crispr_db_torch.py @@ -47,6 +47,13 @@ def jcp_to_standard(x): ] +@batch_processing +def jcp_to_ncbi(x): + return run_query(query=x, input_column="JCP2022", output_column="NCBI_Gene_ID")[0][ + 0 + ] + + def cos_sim(a: Tensor, b: Tensor) -> Tensor: """ Computes the cosine similarity cos_sim(a[i], b[j]) for all i and j. @@ -68,6 +75,9 @@ def cos_sim(a: Tensor, b: Tensor) -> Tensor: url_col = "Metadata_image" # Must start with "Metadata" match_jcp_col = "Match" match_url_col = "Match Example" +std_outname = "Gene/Compound" +ext_links_col = "Match resources" +ncbi_formatter = '{{"href": "https://www.ncbi.nlm.nih.gov/gene/{}", "label":"NCBI"}}' url_template = ( '"https://phenaid.ardigen.com/static-jumpcpexplorer/' 'images/{}_{{}}.jpg"' ) @@ -118,7 +128,7 @@ def cos_sim(a: Tensor, b: Tensor) -> Tensor: { "JCP2022": np.repeat(jcp_ids, n_vals_used * 2), match_jcp_col: jcp_ids[indices.flatten()].astype(" Tensor: mapper_values = parallel(uniq_jcp, jcp_to_standard) mapper = {jcp: std for jcp, std in zip(uniq_jcp, mapper_values)} +ncbi_mapper_values = parallel(uniq_jcp, jcp_to_ncbi) +ncbi_mapper = { + jcp: ncbi_formatter.format(idx) for jcp, idx in zip(uniq_jcp, ncbi_mapper_values) +} + jcp_translated = jcp_df.with_columns( - pl.col("JCP2022").replace(mapper).alias("standard_key"), + pl.col("JCP2022").replace(mapper).alias(std_outname), pl.col(match_jcp_col).replace(mapper), + pl.col(match_jcp_col).replace(ncbi_mapper).alias(ext_links_col), ) -matches_translated = jcp_translated.select(reversed(sorted(jcp_translated.columns))) +matches = jcp_translated.with_columns( + (pl.col("Similarity") * 1000).cast(pl.Int16) +).rename({url_col: f"{std_outname} Example"}) +order = [ + std_outname, + "Match", + "Gene/Compound Example", + match_url_col, + "Similarity", + ext_links_col, + "JCP2022", +] +matches_translated = matches.select(order) final_output = "crispr.parquet" matches_translated.write_parquet(final_output, compression="zstd") @@ -190,7 +218,7 @@ def cos_sim(a: Tensor, b: Tensor) -> Tensor: """ Future plans: -- Add example of matched profile -- Find a way to compress URLs for easier visualisation +- TODO Incorporate NCBI ids (already added on broad-babel) - TODO add differentiating features when compared to their controls +- TODO add Average precision metrics """ From 99a06dff3e18dd80c9c731b2a7a36211e183ad2d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Al=C3=A1n=20F=2E=20Mu=C3=B1oz?= Date: Fri, 26 Jan 2024 19:18:26 -0500 Subject: [PATCH 27/38] deps(all): clean up unused deps --- poetry.lock | 2146 ++++++++++++++++++------------------------------ pyproject.toml | 7 +- 2 files changed, 806 insertions(+), 1347 deletions(-) diff --git a/poetry.lock b/poetry.lock index 0ce8732..d43bdf9 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,32 +1,32 @@ -# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. 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"^0.0.48" From 39e7269d53fb02babf4f428675be4cc7a18cdce7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Al=C3=A1n=20F=2E=20Mu=C3=B1oz?= Date: Fri, 26 Jan 2024 19:19:14 -0500 Subject: [PATCH 28/38] new(RA40B/C): add analysis and csv --- workspace/analysis/RAB40B/compare_features.py | 37 + workspace/analysis/RAB40B/feature_diffs.csv | 4764 +++++++++++++++++ 2 files changed, 4801 insertions(+) create mode 100644 workspace/analysis/RAB40B/compare_features.py create mode 100644 workspace/analysis/RAB40B/feature_diffs.csv diff --git a/workspace/analysis/RAB40B/compare_features.py b/workspace/analysis/RAB40B/compare_features.py new file mode 100644 index 0000000..b9867cd --- /dev/null +++ b/workspace/analysis/RAB40B/compare_features.py @@ -0,0 +1,37 @@ +#!/usr/bin/env jupyter + +# Linked issue: https://github.com/broadinstitute/2023_12_JUMP_data_only_vignettes/issues/4 apply_scaler, +# Compare the four genes of interest that have been shown as anticorrelated: +# - Cluster A: +# - RAB40B +# - RAB40C +# - Cluster B: +# - INSYN1 +# - PIK3R3 + +from pathlib import Path + +import polars as pl + +fpath = Path( + "/dgx1nas1/storage/data/shared/morphmap_profiles/orf/full_profiles_cc_adj_mean_corr.parquet" +) +genes_of_interest = ("RAB40B", "RAB40C", "INSYN1", "PIK3R3") + + +df = pl.read_parquet( fpath ) +sub_df = df.filter(pl.col("Metadata_Symbol").is_in(genes_of_interest)) +groups = dict((x,f"{'RAB40B/C' if x.startswith('RAB') else 'INS/PIK'}") for x in genes_of_interest) + +clusters = sub_df.with_columns(pl.col("Metadata_Symbol").replace(groups).alias("Metadata_Cluster")) + + +medians=clusters.group_by("Metadata_Cluster").median() + wk +diff = medians.select(pl.all().exclude(pl.Utf8).diff()) +to_plot = diff.filter(~pl.all_horizontal(pl.all().is_null())).melt().to_pandas() +import seaborn as sns +import matplotlib.pyplot as plt +sns.scatterplot(data=to_plot, x="variable",y="value") +plt.savefig("distribution.png",dpi=200) +plt.close() diff --git a/workspace/analysis/RAB40B/feature_diffs.csv b/workspace/analysis/RAB40B/feature_diffs.csv new file mode 100644 index 0000000..ae90dcc --- /dev/null +++ b/workspace/analysis/RAB40B/feature_diffs.csv @@ -0,0 +1,4764 @@ +variable,value +Cells_Count,-2.3441414141414043 +Cells_AreaShape_Area,-210.93802749064693 +Cells_AreaShape_BoundingBoxArea,-1096.7966940184137 +Cells_AreaShape_BoundingBoxMaximum_X,-2.163562333985782 +Cells_AreaShape_BoundingBoxMaximum_Y,-1.3448031599126915 +Cells_AreaShape_BoundingBoxMinimum_X,5.654419253341626 +Cells_AreaShape_BoundingBoxMinimum_Y,4.572826468652526 +Cells_AreaShape_Center_X,2.171990567927115 +Cells_AreaShape_Center_Y,1.4380324168115575 +Cells_AreaShape_Compactness,-0.16106048232881282 +Cells_AreaShape_Eccentricity,-0.03260849419590087 +Cells_AreaShape_EquivalentDiameter,-1.4669914425769854 +Cells_AreaShape_EulerNumber,0.000769243443498673 +Cells_AreaShape_Extent,0.027477925502044476 +Cells_AreaShape_FormFactor,0.027303970279070028 +Cells_AreaShape_MajorAxisLength,-9.190166078529066 +Cells_AreaShape_MaxFeretDiameter,-8.784530557991637 +Cells_AreaShape_MaximumRadius,1.1141936682389066 +Cells_AreaShape_MeanRadius,0.404468793765581 +Cells_AreaShape_MedianRadius,0.30973750872092504 +Cells_AreaShape_MinFeretDiameter,-1.0870403606964025 +Cells_AreaShape_MinorAxisLength,0.11411685741343186 +Cells_AreaShape_Orientation,-0.010721260656422576 +Cells_AreaShape_Perimeter,-25.795482051563006 +Cells_AreaShape_Solidity,0.023663969096955034 +Cells_AreaShape_Zernike_0_0,0.038282042374094154 +Cells_AreaShape_Zernike_1_1,0.0024400997788221563 +Cells_AreaShape_Zernike_2_0,0.011080932376631731 +Cells_AreaShape_Zernike_2_2,0.0004923100321046415 +Cells_AreaShape_Zernike_3_1,-0.002311615996572121 +Cells_AreaShape_Zernike_3_3,0.0002537529578712513 +Cells_AreaShape_Zernike_4_0,-0.0005397615187155605 +Cells_AreaShape_Zernike_4_2,-0.0021782995655889328 +Cells_AreaShape_Zernike_4_4,-0.0005101289031996029 +Cells_AreaShape_Zernike_5_1,-0.0007587441749375203 +Cells_AreaShape_Zernike_5_3,-0.00089436844130778 +Cells_AreaShape_Zernike_5_5,0.00010853350162064055 +Cells_AreaShape_Zernike_6_0,-0.00011874938475083744 +Cells_AreaShape_Zernike_6_2,0.00008143226739556897 +Cells_AreaShape_Zernike_6_4,-0.0010374057782509818 +Cells_AreaShape_Zernike_6_6,-0.00021677618508164333 +Cells_AreaShape_Zernike_7_1,-2.43639351642499e-6 +Cells_AreaShape_Zernike_7_3,-0.00023686089860677935 +Cells_AreaShape_Zernike_7_5,-0.0004575393300173841 +Cells_AreaShape_Zernike_7_7,0.00006819751242127529 +Cells_AreaShape_Zernike_8_0,0.00004586882265528156 +Cells_AreaShape_Zernike_8_2,-0.00004535315999088664 +Cells_AreaShape_Zernike_8_4,-0.00010646330397565297 +Cells_AreaShape_Zernike_8_6,-0.0006392983806120499 +Cells_AreaShape_Zernike_8_8,-0.00018498528802522206 +Cells_AreaShape_Zernike_9_1,0.000015534182828478645 +Cells_AreaShape_Zernike_9_3,-0.00004290185843275918 +Cells_AreaShape_Zernike_9_5,-0.00012491445029434644 +Cells_AreaShape_Zernike_9_7,-0.00018280935426490753 +Cells_AreaShape_Zernike_9_9,0.00009160369925461604 +Cells_Children_Cytoplasm_Count,-1.7881393432617188e-6 +Cells_Correlation_Correlation_AGP_DNA,0.11317456809180149 +Cells_Correlation_Correlation_AGP_ER,0.06654455750704844 +Cells_Correlation_Correlation_AGP_Mito,0.07943229991760478 +Cells_Correlation_Correlation_AGP_RNA,0.06563571732907753 +Cells_Correlation_Correlation_DNA_ER,0.012401315604003468 +Cells_Correlation_Correlation_DNA_Mito,0.06071125623108721 +Cells_Correlation_Correlation_DNA_RNA,0.020145927824755314 +Cells_Correlation_Correlation_ER_Mito,0.044346179675703465 +Cells_Correlation_Correlation_ER_RNA,0.0120493578167848 +Cells_Correlation_Correlation_Mito_RNA,0.05119766081028697 +Cells_Correlation_K_AGP_DNA,-0.2851833525819279 +Cells_Correlation_K_AGP_ER,-0.04121708124388501 +Cells_Correlation_K_AGP_Mito,0.07966429481209852 +Cells_Correlation_K_AGP_RNA,0.10077901596836471 +Cells_Correlation_K_DNA_AGP,0.08620491439748318 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+Nuclei_Granularity_1_AGP,-1.9717043084583024 +Nuclei_Granularity_1_DNA,-0.15247552115388485 +Nuclei_Granularity_1_ER,-1.1862287054804428 +Nuclei_Granularity_1_Mito,-4.642165748420295 +Nuclei_Granularity_1_RNA,-1.5934147376997094 +Nuclei_Granularity_2_AGP,-0.14903679840219353 +Nuclei_Granularity_2_DNA,0.11477119320452037 +Nuclei_Granularity_2_ER,-0.1717541736252757 +Nuclei_Granularity_2_Mito,0.18687569530999068 +Nuclei_Granularity_2_RNA,0.17919133581772975 +Nuclei_Granularity_3_AGP,0.06457974224170838 +Nuclei_Granularity_3_DNA,0.38880010977617785 +Nuclei_Granularity_3_ER,0.0813939078305136 +Nuclei_Granularity_3_Mito,0.33660854802395984 +Nuclei_Granularity_3_RNA,0.2586390563155606 +Nuclei_Granularity_4_AGP,0.12957924639482088 +Nuclei_Granularity_4_DNA,0.6458463626583402 +Nuclei_Granularity_4_ER,0.15011709430500164 +Nuclei_Granularity_4_Mito,0.5833093681546067 +Nuclei_Granularity_4_RNA,0.2419199589062303 +Nuclei_Granularity_5_AGP,0.21161160297370157 +Nuclei_Granularity_5_DNA,1.2857079613872453 +Nuclei_Granularity_5_ER,0.35541300842631546 +Nuclei_Granularity_5_Mito,0.6660515488684484 +Nuclei_Granularity_5_RNA,0.21821857093265024 +Nuclei_Granularity_6_AGP,0.5633331865150114 +Nuclei_Granularity_6_DNA,2.4961876300521038 +Nuclei_Granularity_6_ER,0.7379462298315729 +Nuclei_Granularity_6_Mito,0.7589750467586223 +Nuclei_Granularity_6_RNA,0.5441198858331013 +Nuclei_Granularity_7_AGP,1.0130981367701135 +Nuclei_Granularity_7_DNA,1.9587434010923332 +Nuclei_Granularity_7_ER,1.1588472225143622 +Nuclei_Granularity_7_Mito,1.2573406870196335 +Nuclei_Granularity_7_RNA,1.115735872005733 +Nuclei_Granularity_8_AGP,1.6342276131624622 +Nuclei_Granularity_8_DNA,-0.892053484114118 +Nuclei_Granularity_8_ER,1.595864485214514 +Nuclei_Granularity_8_Mito,1.536822222385533 +Nuclei_Granularity_8_RNA,1.488863483003238 +Nuclei_Granularity_9_AGP,1.8042275437419664 +Nuclei_Granularity_9_DNA,-1.742162690565185 +Nuclei_Granularity_9_ER,1.644756704623231 +Nuclei_Granularity_9_Mito,1.603964895579025 +Nuclei_Granularity_9_RNA,1.3509528270715996 +Nuclei_Intensity_IntegratedIntensityEdge_AGP,0.01643155932048493 +Nuclei_Intensity_IntegratedIntensityEdge_DNA,-0.3095657760197198 +Nuclei_Intensity_IntegratedIntensityEdge_ER,-0.1714371681503981 +Nuclei_Intensity_IntegratedIntensityEdge_Mito,0.7538700779579122 +Nuclei_Intensity_IntegratedIntensityEdge_RNA,-0.14317074011422126 +Nuclei_Intensity_IntegratedIntensity_AGP,-0.8690322812162323 +Nuclei_Intensity_IntegratedIntensity_DNA,-10.822329284719851 +Nuclei_Intensity_IntegratedIntensity_ER,-7.183810723862727 +Nuclei_Intensity_IntegratedIntensity_Mito,5.6031090218446975 +Nuclei_Intensity_IntegratedIntensity_RNA,-3.901261307648026 +Nuclei_Intensity_LowerQuartileIntensity_AGP,0.003829975806223321 +Nuclei_Intensity_LowerQuartileIntensity_DNA,-0.004609105762050718 +Nuclei_Intensity_LowerQuartileIntensity_ER,0.0004317842214863355 +Nuclei_Intensity_LowerQuartileIntensity_Mito,0.006676719432093959 +Nuclei_Intensity_LowerQuartileIntensity_RNA,0.004647760631107747 +Nuclei_Intensity_MADIntensity_AGP,0.0013084672039674929 +Nuclei_Intensity_MADIntensity_DNA,0.000028147863058188352 +Nuclei_Intensity_MADIntensity_ER,0.0022577982002777875 +Nuclei_Intensity_MADIntensity_Mito,0.0022406395251918813 +Nuclei_Intensity_MADIntensity_RNA,0.0027670971016824065 +Nuclei_Intensity_MassDisplacement_AGP,0.1442126837892212 +Nuclei_Intensity_MassDisplacement_DNA,-0.01963150707187079 +Nuclei_Intensity_MassDisplacement_ER,0.174529926050897 +Nuclei_Intensity_MassDisplacement_Mito,0.32163427727991234 +Nuclei_Intensity_MassDisplacement_RNA,0.08315616491437808 +Nuclei_Intensity_MaxIntensityEdge_AGP,0.01083357744318426 +Nuclei_Intensity_MaxIntensityEdge_DNA,-0.0008918395023958352 +Nuclei_Intensity_MaxIntensityEdge_ER,0.015009199304737722 +Nuclei_Intensity_MaxIntensityEdge_Mito,0.02417174147723833 +Nuclei_Intensity_MaxIntensityEdge_RNA,0.009375841866183445 +Nuclei_Intensity_MaxIntensity_AGP,0.01054721154933966 +Nuclei_Intensity_MaxIntensity_DNA,-0.009288964623256957 +Nuclei_Intensity_MaxIntensity_ER,0.008775971471604974 +Nuclei_Intensity_MaxIntensity_Mito,0.024934383178826434 +Nuclei_Intensity_MaxIntensity_RNA,0.007875560494092392 +Nuclei_Intensity_MeanIntensityEdge_AGP,0.0033913524280365934 +Nuclei_Intensity_MeanIntensityEdge_DNA,-0.0011920534588324974 +Nuclei_Intensity_MeanIntensityEdge_ER,0.003281459820345004 +Nuclei_Intensity_MeanIntensityEdge_Mito,0.010298157095370337 +Nuclei_Intensity_MeanIntensityEdge_RNA,0.0038317268730653593 +Nuclei_Intensity_MeanIntensity_AGP,0.004625524241305636 +Nuclei_Intensity_MeanIntensity_DNA,-0.004358783015971621 +Nuclei_Intensity_MeanIntensity_ER,0.0030930922648843163 +Nuclei_Intensity_MeanIntensity_Mito,0.010074843575729372 +Nuclei_Intensity_MeanIntensity_RNA,0.006581165269556858 +Nuclei_Intensity_MedianIntensity_AGP,0.004438725972878647 +Nuclei_Intensity_MedianIntensity_DNA,-0.004102387787305671 +Nuclei_Intensity_MedianIntensity_ER,0.002711459080180105 +Nuclei_Intensity_MedianIntensity_Mito,0.008600584211989596 +Nuclei_Intensity_MedianIntensity_RNA,0.007047429338476954 +Nuclei_Intensity_MinIntensityEdge_AGP,0.0019461472811975336 +Nuclei_Intensity_MinIntensityEdge_DNA,-0.0008468257793361731 +Nuclei_Intensity_MinIntensityEdge_ER,-0.0012647794432243571 +Nuclei_Intensity_MinIntensityEdge_Mito,0.0035308628379129804 +Nuclei_Intensity_MinIntensityEdge_RNA,0.0018127218640590255 +Nuclei_Intensity_MinIntensity_AGP,0.0020040932946536936 +Nuclei_Intensity_MinIntensity_DNA,-0.000857472528214501 +Nuclei_Intensity_MinIntensity_ER,-0.0012328099950026225 +Nuclei_Intensity_MinIntensity_Mito,0.0035647717203988044 +Nuclei_Intensity_MinIntensity_RNA,0.001973019228101917 +Nuclei_Intensity_StdIntensityEdge_AGP,0.002734078563132411 +Nuclei_Intensity_StdIntensityEdge_DNA,-0.00008135364626326951 +Nuclei_Intensity_StdIntensityEdge_ER,0.006072481413758287 +Nuclei_Intensity_StdIntensityEdge_Mito,0.0065897443820394155 +Nuclei_Intensity_StdIntensityEdge_RNA,0.003627356981830746 +Nuclei_Intensity_StdIntensity_AGP,0.0023046261564111294 +Nuclei_Intensity_StdIntensity_DNA,-0.001411640642827134 +Nuclei_Intensity_StdIntensity_ER,0.002975996426559542 +Nuclei_Intensity_StdIntensity_Mito,0.004684819887543985 +Nuclei_Intensity_StdIntensity_RNA,0.0024746750428598055 +Nuclei_Intensity_UpperQuartileIntensity_AGP,0.005551785416257258 +Nuclei_Intensity_UpperQuartileIntensity_DNA,-0.004475139198635153 +Nuclei_Intensity_UpperQuartileIntensity_ER,0.005461399141035973 +Nuclei_Intensity_UpperQuartileIntensity_Mito,0.01201752956909307 +Nuclei_Intensity_UpperQuartileIntensity_RNA,0.009039973480975455 +Nuclei_Location_CenterMassIntensity_X_AGP,2.0223942194009554 +Nuclei_Location_CenterMassIntensity_X_DNA,1.9567254641836902 +Nuclei_Location_CenterMassIntensity_X_ER,1.964855889201111 +Nuclei_Location_CenterMassIntensity_X_Mito,1.9988290833029225 +Nuclei_Location_CenterMassIntensity_X_RNA,1.9698590587023563 +Nuclei_Location_CenterMassIntensity_Y_AGP,1.45603387541134 +Nuclei_Location_CenterMassIntensity_Y_DNA,1.498969735833514 +Nuclei_Location_CenterMassIntensity_Y_ER,1.4345942692944045 +Nuclei_Location_CenterMassIntensity_Y_Mito,1.4528460858070495 +Nuclei_Location_CenterMassIntensity_Y_RNA,1.4658684250214402 +Nuclei_Location_CenterMassIntensity_Z_AGP,0.0 +Nuclei_Location_CenterMassIntensity_Z_DNA,0.0 +Nuclei_Location_CenterMassIntensity_Z_ER,0.0 +Nuclei_Location_CenterMassIntensity_Z_Mito,0.0 +Nuclei_Location_CenterMassIntensity_Z_RNA,0.0 +Nuclei_Location_Center_X,1.9458065510737175 +Nuclei_Location_Center_Y,1.5408128762891515 +Nuclei_Location_Center_Z,0.0 +Nuclei_Location_MaxIntensity_X_AGP,1.8972030722061213 +Nuclei_Location_MaxIntensity_X_DNA,1.9555629824240421 +Nuclei_Location_MaxIntensity_X_ER,1.990245021647727 +Nuclei_Location_MaxIntensity_X_Mito,1.9196900141080366 +Nuclei_Location_MaxIntensity_X_RNA,1.773404013525564 +Nuclei_Location_MaxIntensity_Y_AGP,1.505455180117523 +Nuclei_Location_MaxIntensity_Y_DNA,1.2855941297233593 +Nuclei_Location_MaxIntensity_Y_ER,1.2606076308110348 +Nuclei_Location_MaxIntensity_Y_Mito,1.0825387279332426 +Nuclei_Location_MaxIntensity_Y_RNA,1.502248688432722 +Nuclei_Location_MaxIntensity_Z_AGP,0.0 +Nuclei_Location_MaxIntensity_Z_DNA,0.0 +Nuclei_Location_MaxIntensity_Z_ER,0.0 +Nuclei_Location_MaxIntensity_Z_Mito,0.0 +Nuclei_Location_MaxIntensity_Z_RNA,0.0 +Nuclei_Neighbors_AngleBetweenNeighbors_1,-0.3310192428376869 +Nuclei_Neighbors_FirstClosestDistance_1,0.9098170546736092 +Nuclei_Neighbors_FirstClosestObjectNumber_1,-0.6705452486044208 +Nuclei_Neighbors_NumberOfNeighbors_1,-0.007876676618341827 +Nuclei_Neighbors_PercentTouching_1,0.09700612990982371 +Nuclei_Neighbors_SecondClosestDistance_1,1.480876822843475 +Nuclei_Neighbors_SecondClosestObjectNumber_1,-0.5011810047760323 +Nuclei_Number_Object_Number,-0.635021273719006 +Nuclei_ObjectSkeleton_NumberBranchEnds_mito_skel,-1.967278609619418 +Nuclei_ObjectSkeleton_NumberNonTrunkBranches_mito_skel,-3.70442236746487 +Nuclei_ObjectSkeleton_NumberTrunks_mito_skel,-0.797397897305308 +Nuclei_ObjectSkeleton_TotalObjectSkeletonLength_mito_skel,-46.32449024969072 +Nuclei_Parent_NucleiIncludingEdges,-1.8973369306322891 +Nuclei_RadialDistribution_FracAtD_AGP_1of4,0.0038292480905468326 +Nuclei_RadialDistribution_FracAtD_AGP_2of4,0.009911890744990713 +Nuclei_RadialDistribution_FracAtD_AGP_3of4,0.005837269441865018 +Nuclei_RadialDistribution_FracAtD_AGP_4of4,-0.01996211657197747 +Nuclei_RadialDistribution_FracAtD_DNA_1of4,0.006132947561712465 +Nuclei_RadialDistribution_FracAtD_DNA_2of4,0.015514929180338456 +Nuclei_RadialDistribution_FracAtD_DNA_3of4,0.003461324227555503 +Nuclei_RadialDistribution_FracAtD_DNA_4of4,-0.024332188238703194 +Nuclei_RadialDistribution_FracAtD_ER_1of4,0.0038331270809381065 +Nuclei_RadialDistribution_FracAtD_ER_2of4,0.008563045385671837 +Nuclei_RadialDistribution_FracAtD_ER_3of4,6.853301556507003e-6 +Nuclei_RadialDistribution_FracAtD_ER_4of4,-0.0122521681802897 +Nuclei_RadialDistribution_FracAtD_Mito_1of4,0.002678267853499584 +Nuclei_RadialDistribution_FracAtD_Mito_2of4,0.004574515946960067 +Nuclei_RadialDistribution_FracAtD_Mito_3of4,-0.0010319793481378043 +Nuclei_RadialDistribution_FracAtD_Mito_4of4,-0.006816921616654876 +Nuclei_RadialDistribution_FracAtD_RNA_1of4,0.004677820717445255 +Nuclei_RadialDistribution_FracAtD_RNA_2of4,0.011153942630139038 +Nuclei_RadialDistribution_FracAtD_RNA_3of4,0.00040409527568573903 +Nuclei_RadialDistribution_FracAtD_RNA_4of4,-0.016521296287336097 +Nuclei_RadialDistribution_MeanFrac_AGP_1of4,0.016526805296464206 +Nuclei_RadialDistribution_MeanFrac_AGP_2of4,0.019609713404409645 +Nuclei_RadialDistribution_MeanFrac_AGP_3of4,0.013022857769475286 +Nuclei_RadialDistribution_MeanFrac_AGP_4of4,-0.02066300760855811 +Nuclei_RadialDistribution_MeanFrac_DNA_1of4,0.05262788313040642 +Nuclei_RadialDistribution_MeanFrac_DNA_2of4,0.04004039073207584 +Nuclei_RadialDistribution_MeanFrac_DNA_3of4,0.0057941120085649766 +Nuclei_RadialDistribution_MeanFrac_DNA_4of4,-0.03718595668982539 +Nuclei_RadialDistribution_MeanFrac_ER_1of4,0.002829521733779816 +Nuclei_RadialDistribution_MeanFrac_ER_2of4,-0.0017500142783610593 +Nuclei_RadialDistribution_MeanFrac_ER_3of4,-0.004827675469863232 +Nuclei_RadialDistribution_MeanFrac_ER_4of4,-0.006796828580248262 +Nuclei_RadialDistribution_MeanFrac_Mito_1of4,-0.002892264307935096 +Nuclei_RadialDistribution_MeanFrac_Mito_2of4,-0.007840887273729228 +Nuclei_RadialDistribution_MeanFrac_Mito_3of4,-0.008167678084980175 +Nuclei_RadialDistribution_MeanFrac_Mito_4of4,0.007421285148291996 +Nuclei_RadialDistribution_MeanFrac_RNA_1of4,0.01221950147035787 +Nuclei_RadialDistribution_MeanFrac_RNA_2of4,0.009761230751319388 +Nuclei_RadialDistribution_MeanFrac_RNA_3of4,-0.003671756667545229 +Nuclei_RadialDistribution_MeanFrac_RNA_4of4,-0.008872154448352593 +Nuclei_RadialDistribution_RadialCV_AGP_1of4,0.002828573379903058 +Nuclei_RadialDistribution_RadialCV_AGP_2of4,0.009677715795956214 +Nuclei_RadialDistribution_RadialCV_AGP_3of4,0.021000488032933314 +Nuclei_RadialDistribution_RadialCV_AGP_4of4,0.03212144340531615 +Nuclei_RadialDistribution_RadialCV_DNA_1of4,0.002199855333318685 +Nuclei_RadialDistribution_RadialCV_DNA_2of4,0.0034137407854633994 +Nuclei_RadialDistribution_RadialCV_DNA_3of4,0.005827422127539748 +Nuclei_RadialDistribution_RadialCV_DNA_4of4,0.0019763658081860674 +Nuclei_RadialDistribution_RadialCV_ER_1of4,-0.0027418830765084832 +Nuclei_RadialDistribution_RadialCV_ER_2of4,0.008382653833428517 +Nuclei_RadialDistribution_RadialCV_ER_3of4,0.020243736262258047 +Nuclei_RadialDistribution_RadialCV_ER_4of4,0.04337517611648053 +Nuclei_RadialDistribution_RadialCV_Mito_1of4,0.00482536734829449 +Nuclei_RadialDistribution_RadialCV_Mito_2of4,0.015835565963058712 +Nuclei_RadialDistribution_RadialCV_Mito_3of4,0.0368550124190737 +Nuclei_RadialDistribution_RadialCV_Mito_4of4,0.06533181008828468 +Nuclei_RadialDistribution_RadialCV_RNA_1of4,0.002487605331758456 +Nuclei_RadialDistribution_RadialCV_RNA_2of4,0.011416226213276013 +Nuclei_RadialDistribution_RadialCV_RNA_3of4,0.0142201366378157 +Nuclei_RadialDistribution_RadialCV_RNA_4of4,0.025185399593213302 +Nuclei_Texture_AngularSecondMoment_AGP_10_00_256,-0.008140609587022984 +Nuclei_Texture_AngularSecondMoment_AGP_10_01_256,-0.007707237800016991 +Nuclei_Texture_AngularSecondMoment_AGP_10_02_256,-0.008034118293601249 +Nuclei_Texture_AngularSecondMoment_AGP_10_03_256,-0.007692582471099661 +Nuclei_Texture_AngularSecondMoment_AGP_3_00_256,-0.009227579748464582 +Nuclei_Texture_AngularSecondMoment_AGP_3_01_256,-0.008596395067528535 +Nuclei_Texture_AngularSecondMoment_AGP_3_02_256,-0.009184717681207842 +Nuclei_Texture_AngularSecondMoment_AGP_3_03_256,-0.008423409649325107 +Nuclei_Texture_AngularSecondMoment_AGP_5_00_256,-0.008354481273273394 +Nuclei_Texture_AngularSecondMoment_AGP_5_01_256,-0.008463138990233493 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+Nuclei_Texture_Variance_RNA_5_03_256,7.041102272489917 From 5c1661c67ab24334ee81daa9f2281caa36a14d34 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Al=C3=A1n=20F=2E=20Mu=C3=B1oz?= Date: Fri, 26 Jan 2024 19:22:40 -0500 Subject: [PATCH 29/38] change(RAB40B): sort output csv --- workspace/analysis/RAB40B/feature_diffs.csv | 9382 ++++++++--------- .../distances/gen_cosine_crispr_db_torch.py | 1 + 2 files changed, 4692 insertions(+), 4691 deletions(-) diff --git a/workspace/analysis/RAB40B/feature_diffs.csv b/workspace/analysis/RAB40B/feature_diffs.csv index ae90dcc..f76c12c 100644 --- a/workspace/analysis/RAB40B/feature_diffs.csv +++ b/workspace/analysis/RAB40B/feature_diffs.csv @@ -1,4764 +1,4764 @@ variable,value -Cells_Count,-2.3441414141414043 -Cells_AreaShape_Area,-210.93802749064693 +Image_ImageQuality_TotalIntensity_OrigER,-5613.696709018493 +Image_Intensity_TotalIntensity_ER,-3833.235826956485 +Image_ImageQuality_TotalIntensity_OrigRNA,-3536.8689108573526 +Image_ImageQuality_TotalIntensity_OrigDNA,-2527.0657533979547 +Image_Intensity_TotalIntensity_RNA,-2213.4114626345636 +Image_Intensity_TotalIntensity_DNA,-1300.263332913209 Cells_AreaShape_BoundingBoxArea,-1096.7966940184137 -Cells_AreaShape_BoundingBoxMaximum_X,-2.163562333985782 -Cells_AreaShape_BoundingBoxMaximum_Y,-1.3448031599126915 -Cells_AreaShape_BoundingBoxMinimum_X,5.654419253341626 -Cells_AreaShape_BoundingBoxMinimum_Y,4.572826468652526 -Cells_AreaShape_Center_X,2.171990567927115 -Cells_AreaShape_Center_Y,1.4380324168115575 -Cells_AreaShape_Compactness,-0.16106048232881282 -Cells_AreaShape_Eccentricity,-0.03260849419590087 -Cells_AreaShape_EquivalentDiameter,-1.4669914425769854 -Cells_AreaShape_EulerNumber,0.000769243443498673 -Cells_AreaShape_Extent,0.027477925502044476 -Cells_AreaShape_FormFactor,0.027303970279070028 +Cytoplasm_AreaShape_BoundingBoxArea,-1096.7966940184137 +Image_ImageQuality_TotalIntensity_OrigAGP,-892.5614384577248 +Cells_AreaShape_Area,-210.93802749064693 +Nuclei_AreaShape_BoundingBoxArea,-184.05421884336397 +Image_Intensity_TotalIntensity_AGP,-125.21779337076282 +Nuclei_AreaShape_Area,-109.37232339615385 +Cytoplasm_AreaShape_Area,-97.22765017848901 +Cells_Texture_SumVariance_DNA_3_00_256,-53.54443928893265 +Cells_Texture_SumVariance_DNA_3_01_256,-53.487071645349516 +Cells_Texture_SumVariance_DNA_5_00_256,-53.28370653155403 +Cells_Texture_SumVariance_DNA_3_02_256,-53.128087904746906 +Cells_Texture_SumVariance_DNA_3_03_256,-53.00722469170604 +Cells_Texture_SumVariance_DNA_5_02_256,-52.87611408809265 +Cells_Texture_SumVariance_DNA_5_01_256,-52.81455300148737 +Cells_Texture_SumVariance_DNA_5_03_256,-51.68103942863223 +Cells_Texture_SumVariance_DNA_10_00_256,-50.49691360235282 +Cells_Texture_SumVariance_DNA_10_02_256,-50.43133265793479 +Cells_Texture_SumVariance_DNA_10_01_256,-47.186224066091725 +Nuclei_ObjectSkeleton_TotalObjectSkeletonLength_mito_skel,-46.32449024969072 +Cells_Texture_SumVariance_DNA_10_03_256,-44.43547722024775 +Cells_Intensity_IntegratedIntensity_ER,-37.06701220315101 +Nuclei_Texture_SumVariance_DNA_3_02_256,-35.96207431504891 +Nuclei_Texture_SumVariance_DNA_3_00_256,-33.45161654933821 +Nuclei_Texture_SumVariance_DNA_3_01_256,-33.42531537566677 +Cytoplasm_AreaShape_Perimeter,-32.30741378121763 +Nuclei_Texture_SumVariance_DNA_5_02_256,-30.196375587432257 +Nuclei_Texture_SumVariance_DNA_3_03_256,-29.58766011006165 +Image_Texture_SumVariance_DNA_3_02_256,-27.284803277536838 +Nuclei_Texture_SumVariance_DNA_5_00_256,-27.167277668014847 +Image_Texture_SumVariance_DNA_3_00_256,-27.074349210083838 +Cytoplasm_Intensity_IntegratedIntensity_ER,-26.65422084451093 +Image_Texture_SumVariance_DNA_3_01_256,-26.512784938402746 +Nuclei_Texture_Contrast_DNA_10_01_256,-26.47281816485095 +Image_Texture_SumVariance_DNA_3_03_256,-26.20951703669744 +Image_Texture_SumVariance_DNA_5_02_256,-26.049659456307126 +Cells_AreaShape_Perimeter,-25.795482051563006 +Image_Texture_SumVariance_DNA_5_00_256,-25.53919527843773 +Image_Texture_SumVariance_DNA_5_01_256,-24.689520610773513 +Image_Texture_SumVariance_DNA_5_03_256,-24.14755008773718 +Nuclei_Texture_SumVariance_DNA_5_01_256,-23.65515483514612 +Image_Texture_SumVariance_DNA_10_02_256,-23.18773840666445 +Cells_Texture_Contrast_DNA_10_03_256,-22.622579566021415 +Cells_Texture_Contrast_DNA_10_01_256,-22.421691306576548 +Image_Texture_SumVariance_DNA_10_00_256,-21.859430296872155 +Nuclei_Texture_SumVariance_DNA_5_03_256,-21.258620843660353 +Image_Texture_SumVariance_DNA_10_01_256,-20.680510319671612 +Nuclei_Texture_SumVariance_DNA_10_02_256,-20.384803263506726 +Image_Texture_SumVariance_DNA_10_03_256,-19.815233042444568 +Nuclei_Texture_Contrast_DNA_5_01_256,-19.710201207153233 +Nuclei_Texture_Contrast_DNA_10_02_256,-19.452246449755243 +Nuclei_Texture_Contrast_DNA_10_00_256,-18.165420112741756 +Cells_Texture_Variance_DNA_10_01_256,-17.40085155620077 +Cells_Texture_Variance_DNA_10_00_256,-17.219912903398814 +Nuclei_Texture_SumVariance_DNA_10_00_256,-17.17434600749546 +Nuclei_Texture_Contrast_DNA_10_03_256,-17.009888425054683 +Cells_Texture_Variance_DNA_10_03_256,-16.767132002793083 +Cells_Intensity_IntegratedIntensity_RNA,-16.6329745440802 +Cells_Texture_Variance_DNA_10_02_256,-16.429384176363357 +Cytoplasm_Intensity_IntegratedIntensity_RNA,-15.984364000186112 +Cells_Texture_Variance_DNA_5_03_256,-15.961549482832446 +Cells_Texture_Variance_DNA_5_01_256,-15.623117400811424 +Cells_Texture_Contrast_DNA_10_00_256,-15.401693286714725 +Cells_Texture_Contrast_DNA_10_02_256,-15.27757399302753 +Cells_Texture_Variance_DNA_5_00_256,-15.055158716334805 +Cells_Texture_Variance_DNA_5_02_256,-14.748981485109574 +Cells_Texture_Variance_DNA_3_03_256,-14.65666052949059 +Cells_Texture_Variance_DNA_3_01_256,-14.469036268011802 +Cells_Texture_Variance_DNA_3_00_256,-14.09463988856453 +Cells_Texture_Variance_DNA_3_02_256,-13.94116146728813 +Cytoplasm_Intensity_IntegratedIntensity_AGP,-13.605230616065032 +Nuclei_Texture_Variance_DNA_10_00_256,-12.631051050322235 +Nuclei_Texture_SumVariance_DNA_10_03_256,-12.575898481361811 +Nuclei_Texture_Contrast_DNA_5_00_256,-12.572938372627537 +Nuclei_Texture_Variance_DNA_5_03_256,-12.006371797553754 +Nuclei_Texture_Variance_DNA_10_02_256,-11.886518720546473 +Nuclei_Texture_Variance_DNA_5_01_256,-11.492995206877417 +Nuclei_Texture_Variance_DNA_5_02_256,-11.467477362641752 +Nuclei_Texture_Contrast_DNA_3_01_256,-11.327913289451125 +Nuclei_Texture_Variance_DNA_3_03_256,-11.316147684222155 +Nuclei_Texture_Variance_DNA_5_00_256,-11.254598618018871 +Nuclei_Texture_Variance_DNA_3_01_256,-11.18804802521879 +Nuclei_Texture_Variance_DNA_3_02_256,-10.981009942474184 +Nuclei_Texture_SumVariance_DNA_10_01_256,-10.9341033577805 +Nuclei_Intensity_IntegratedIntensity_DNA,-10.822329284719851 +Cells_Intensity_IntegratedIntensity_DNA,-10.725060794402822 +Nuclei_Texture_Variance_DNA_3_00_256,-10.6483301551423 +Cytoplasm_AreaShape_MajorAxisLength,-10.343641814764803 +Nuclei_Texture_Variance_DNA_10_01_256,-10.294112662245606 +Cells_Texture_Contrast_DNA_5_01_256,-9.706689098219913 +Cells_Texture_Contrast_DNA_5_03_256,-9.654863209116538 +Nuclei_Texture_Contrast_DNA_5_03_256,-9.227232944623402 Cells_AreaShape_MajorAxisLength,-9.190166078529066 +Image_Texture_Contrast_DNA_10_03_256,-8.791733545439875 Cells_AreaShape_MaxFeretDiameter,-8.784530557991637 -Cells_AreaShape_MaximumRadius,1.1141936682389066 -Cells_AreaShape_MeanRadius,0.404468793765581 -Cells_AreaShape_MedianRadius,0.30973750872092504 +Cytoplasm_AreaShape_MaxFeretDiameter,-8.784530557991637 +Cells_Intensity_IntegratedIntensity_AGP,-8.726041726487477 +Nuclei_AreaShape_Perimeter,-8.674868186702492 +Nuclei_Texture_Contrast_DNA_3_03_256,-8.10563827522774 +Image_Texture_Contrast_DNA_10_01_256,-7.891671101929845 +Nuclei_Texture_Variance_DNA_10_03_256,-7.397653669376815 +Nuclei_Texture_Contrast_DNA_3_00_256,-7.236150635823099 +Nuclei_Intensity_IntegratedIntensity_ER,-7.183810723862727 +Image_Texture_Variance_DNA_10_02_256,-7.155349191159628 +Image_Texture_Variance_DNA_10_03_256,-7.151636604571811 +Image_Texture_Variance_DNA_10_01_256,-7.144260143770376 +Image_Texture_Variance_DNA_5_03_256,-7.124997307928414 +Image_Texture_Variance_DNA_5_01_256,-7.122478419685351 +Image_Texture_Variance_DNA_5_02_256,-7.119000414163336 +Image_Texture_Variance_DNA_3_03_256,-7.116406124603765 +Image_Texture_Variance_DNA_3_01_256,-7.115311735597084 +Image_Texture_Variance_DNA_3_02_256,-7.1120754283581125 +Image_Texture_Variance_DNA_5_00_256,-7.098948613597063 +Image_Texture_Variance_DNA_3_00_256,-7.097780201501061 +Image_Texture_Variance_DNA_10_00_256,-7.088873804585786 +Nuclei_Texture_Contrast_DNA_5_02_256,-7.012188202394523 +Image_Texture_Contrast_DNA_10_00_256,-6.4932138545819615 +Cells_Texture_Contrast_DNA_5_02_256,-6.114802872375737 +Cells_Neighbors_PercentTouching_Adjacent,-5.535020604535743 +Image_Texture_Contrast_DNA_10_02_256,-5.436644064931642 +Cells_Texture_Contrast_DNA_5_00_256,-5.287203117450996 +Cytoplasm_Intensity_IntegratedIntensity_DNA,-4.799600506759595 +Nuclei_Granularity_1_Mito,-4.642165748420295 +Cells_Neighbors_PercentTouching_5,-4.510961063675751 +Nuclei_Texture_Contrast_DNA_3_02_256,-4.441399299617284 +Cells_Texture_Contrast_DNA_3_01_256,-4.394236702368731 +Image_Texture_Contrast_DNA_5_03_256,-4.358341203003508 +Cells_Texture_Contrast_DNA_3_03_256,-4.227397858627476 +Nuclei_AreaShape_MajorAxisLength,-3.982642404817386 +Nuclei_AreaShape_MaxFeretDiameter,-3.9465948453074584 +Nuclei_Intensity_IntegratedIntensity_RNA,-3.901261307648026 +Image_Texture_Contrast_DNA_5_01_256,-3.8062788485672217 +Nuclei_ObjectSkeleton_NumberNonTrunkBranches_mito_skel,-3.70442236746487 +Cells_Granularity_1_Mito,-3.0544891599066037 +Image_Texture_Contrast_DNA_5_00_256,-2.8546458921783326 +Cells_Texture_Contrast_DNA_3_02_256,-2.63025624598487 +Image_Texture_Contrast_DNA_5_02_256,-2.432372690129691 +Nuclei_Texture_SumAverage_DNA_10_02_256,-2.389395139276324 +Nuclei_Texture_SumAverage_DNA_10_00_256,-2.3583165805611728 +Nuclei_Texture_SumAverage_DNA_10_01_256,-2.3519258326991004 +Cells_Count,-2.3441414141414043 +Nuclei_Texture_SumAverage_DNA_3_00_256,-2.331809995805431 +Nuclei_Texture_SumAverage_DNA_5_00_256,-2.330100944329841 +Nuclei_Texture_SumAverage_DNA_3_03_256,-2.3091596571025885 +Nuclei_Texture_SumAverage_DNA_5_03_256,-2.307502072919471 +Nuclei_Texture_SumAverage_DNA_5_02_256,-2.280916829465154 +Nuclei_Texture_SumAverage_DNA_3_02_256,-2.277765586590366 +Nuclei_Texture_SumAverage_DNA_3_01_256,-2.2749262560379506 +Nuclei_Texture_SumAverage_DNA_5_01_256,-2.2672842907945707 +Image_Texture_Contrast_DNA_3_03_256,-2.2536593493976826 +Nuclei_Texture_SumAverage_DNA_10_03_256,-2.253632790282314 +Cells_AreaShape_BoundingBoxMaximum_X,-2.163562333985782 +Cytoplasm_AreaShape_BoundingBoxMaximum_X,-2.163562333985782 +Image_Granularity_9_DNA,-2.152729617467002 +Cytoplasm_Granularity_1_Mito,-2.1366268821761536 +Cells_Texture_Contrast_DNA_3_00_256,-2.1174363881599 +Nuclei_Granularity_1_AGP,-1.9717043084583024 +Nuclei_ObjectSkeleton_NumberBranchEnds_mito_skel,-1.967278609619418 +Image_Texture_Contrast_DNA_3_01_256,-1.9504235366983043 +Nuclei_AreaShape_EquivalentDiameter,-1.902086645447584 +Nuclei_Parent_NucleiIncludingEdges,-1.8973369306322891 +Cells_Parent_CellsIncludingEdges,-1.89718840906828 +Nuclei_Granularity_9_DNA,-1.742162690565185 +Image_Texture_SumAverage_ER_10_02_256,-1.5986161489911153 +Image_Texture_SumAverage_ER_5_02_256,-1.5972671403840335 +Image_Texture_SumAverage_ER_10_01_256,-1.5969772751075992 +Image_Texture_SumAverage_ER_10_03_256,-1.5969500596706512 +Image_Texture_SumAverage_ER_3_02_256,-1.5968033593604385 +Image_Texture_SumAverage_ER_5_03_256,-1.5953093063762054 +Image_Texture_SumAverage_ER_5_01_256,-1.5953018213969516 +Image_Texture_SumAverage_ER_10_00_256,-1.5951109566103083 +Image_Texture_SumAverage_ER_3_03_256,-1.5949254138889142 +Image_Texture_SumAverage_ER_3_01_256,-1.5949192373597256 +Image_Texture_SumAverage_ER_3_00_256,-1.5942776956194193 +Nuclei_Granularity_1_RNA,-1.5934147376997094 +Image_Texture_SumAverage_ER_5_00_256,-1.5931824466639235 +Image_Granularity_10_DNA,-1.5484921342722298 +Cells_AreaShape_EquivalentDiameter,-1.4669914425769854 +Cytoplasm_Intensity_IntegratedIntensityEdge_ER,-1.381848570002734 +Cells_AreaShape_BoundingBoxMaximum_Y,-1.3448031599126915 +Cytoplasm_AreaShape_BoundingBoxMaximum_Y,-1.3448031599126915 +Image_Texture_Contrast_DNA_3_00_256,-1.31471293097481 +Cells_Granularity_9_DNA,-1.2276411244807364 +Nuclei_Granularity_1_ER,-1.1862287054804428 +Image_Texture_Contrast_DNA_3_02_256,-1.159624453030524 +Nuclei_Granularity_10_DNA,-1.1239274600466198 +Cytoplasm_AreaShape_MinFeretDiameter,-1.0870403624643772 Cells_AreaShape_MinFeretDiameter,-1.0870403606964025 -Cells_AreaShape_MinorAxisLength,0.11411685741343186 -Cells_AreaShape_Orientation,-0.010721260656422576 -Cells_AreaShape_Perimeter,-25.795482051563006 -Cells_AreaShape_Solidity,0.023663969096955034 -Cells_AreaShape_Zernike_0_0,0.038282042374094154 -Cells_AreaShape_Zernike_1_1,0.0024400997788221563 -Cells_AreaShape_Zernike_2_0,0.011080932376631731 -Cells_AreaShape_Zernike_2_2,0.0004923100321046415 -Cells_AreaShape_Zernike_3_1,-0.002311615996572121 -Cells_AreaShape_Zernike_3_3,0.0002537529578712513 -Cells_AreaShape_Zernike_4_0,-0.0005397615187155605 -Cells_AreaShape_Zernike_4_2,-0.0021782995655889328 -Cells_AreaShape_Zernike_4_4,-0.0005101289031996029 -Cells_AreaShape_Zernike_5_1,-0.0007587441749375203 -Cells_AreaShape_Zernike_5_3,-0.00089436844130778 -Cells_AreaShape_Zernike_5_5,0.00010853350162064055 -Cells_AreaShape_Zernike_6_0,-0.00011874938475083744 -Cells_AreaShape_Zernike_6_2,0.00008143226739556897 -Cells_AreaShape_Zernike_6_4,-0.0010374057782509818 -Cells_AreaShape_Zernike_6_6,-0.00021677618508164333 -Cells_AreaShape_Zernike_7_1,-2.43639351642499e-6 -Cells_AreaShape_Zernike_7_3,-0.00023686089860677935 -Cells_AreaShape_Zernike_7_5,-0.0004575393300173841 -Cells_AreaShape_Zernike_7_7,0.00006819751242127529 -Cells_AreaShape_Zernike_8_0,0.00004586882265528156 -Cells_AreaShape_Zernike_8_2,-0.00004535315999088664 -Cells_AreaShape_Zernike_8_4,-0.00010646330397565297 -Cells_AreaShape_Zernike_8_6,-0.0006392983806120499 -Cells_AreaShape_Zernike_8_8,-0.00018498528802522206 -Cells_AreaShape_Zernike_9_1,0.000015534182828478645 -Cells_AreaShape_Zernike_9_3,-0.00004290185843275918 -Cells_AreaShape_Zernike_9_5,-0.00012491445029434644 -Cells_AreaShape_Zernike_9_7,-0.00018280935426490753 -Cells_AreaShape_Zernike_9_9,0.00009160369925461604 -Cells_Children_Cytoplasm_Count,-1.7881393432617188e-6 -Cells_Correlation_Correlation_AGP_DNA,0.11317456809180149 -Cells_Correlation_Correlation_AGP_ER,0.06654455750704844 -Cells_Correlation_Correlation_AGP_Mito,0.07943229991760478 -Cells_Correlation_Correlation_AGP_RNA,0.06563571732907753 -Cells_Correlation_Correlation_DNA_ER,0.012401315604003468 -Cells_Correlation_Correlation_DNA_Mito,0.06071125623108721 -Cells_Correlation_Correlation_DNA_RNA,0.020145927824755314 -Cells_Correlation_Correlation_ER_Mito,0.044346179675703465 -Cells_Correlation_Correlation_ER_RNA,0.0120493578167848 -Cells_Correlation_Correlation_Mito_RNA,0.05119766081028697 -Cells_Correlation_K_AGP_DNA,-0.2851833525819279 -Cells_Correlation_K_AGP_ER,-0.04121708124388501 -Cells_Correlation_K_AGP_Mito,0.07966429481209852 -Cells_Correlation_K_AGP_RNA,0.10077901596836471 -Cells_Correlation_K_DNA_AGP,0.08620491439748318 -Cells_Correlation_K_DNA_ER,-0.03547980689927098 -Cells_Correlation_K_DNA_Mito,-0.055762577354767216 -Cells_Correlation_K_DNA_RNA,0.09289117681317975 -Cells_Correlation_K_ER_AGP,0.016265400194170485 -Cells_Correlation_K_ER_DNA,0.06079464239230483 -Cells_Correlation_K_ER_Mito,0.01941279137042385 -Cells_Correlation_K_ER_RNA,0.04845418914413316 -Cells_Correlation_K_Mito_AGP,-0.11455503702679048 -Cells_Correlation_K_Mito_DNA,0.1603151981986593 -Cells_Correlation_K_Mito_ER,0.008875677927848713 -Cells_Correlation_K_Mito_RNA,-0.023027822859265162 -Cells_Correlation_K_RNA_AGP,-0.03216064538883674 -Cells_Correlation_K_RNA_DNA,-0.0875233599140433 -Cells_Correlation_K_RNA_ER,-0.02800213835834567 -Cells_Correlation_K_RNA_Mito,0.008185500707063323 -Cells_Correlation_Manders_AGP_DNA,-0.011530157554150948 -Cells_Correlation_Manders_AGP_ER,-0.06872825929061349 -Cells_Correlation_Manders_AGP_Mito,-0.09621974598887396 -Cells_Correlation_Manders_AGP_RNA,-0.09251023286081633 -Cells_Correlation_Manders_DNA_AGP,0.0008367885925382346 -Cells_Correlation_Manders_DNA_ER,0.00012595801155175165 -Cells_Correlation_Manders_DNA_Mito,0.002168230950378489 -Cells_Correlation_Manders_DNA_RNA,0.00007801378551210505 -Cells_Correlation_Manders_ER_AGP,0.0008549467714931774 -Cells_Correlation_Manders_ER_DNA,0.013605966366466674 -Cells_Correlation_Manders_ER_Mito,-0.028388575310930142 -Cells_Correlation_Manders_ER_RNA,-0.009413907337858574 -Cells_Correlation_Manders_Mito_AGP,0.0040526604965515555 -Cells_Correlation_Manders_Mito_DNA,0.03353323699442803 -Cells_Correlation_Manders_Mito_ER,-0.0012821904741793337 -Cells_Correlation_Manders_Mito_RNA,-0.0171660015010449 -Cells_Correlation_Manders_RNA_AGP,0.0018978955610010192 -Cells_Correlation_Manders_RNA_DNA,0.016351140438716424 -Cells_Correlation_Manders_RNA_ER,-0.0048902350788596035 -Cells_Correlation_Manders_RNA_Mito,-0.026476328007142936 -Cells_Correlation_Overlap_AGP_DNA,0.007396430778727158 -Cells_Correlation_Overlap_AGP_ER,0.004362560759081692 -Cells_Correlation_Overlap_AGP_Mito,-0.0030365839233167727 -Cells_Correlation_Overlap_AGP_RNA,0.003527250689897526 -Cells_Correlation_Overlap_DNA_ER,-0.0044488445868855475 -Cells_Correlation_Overlap_DNA_Mito,-0.012374271883722598 -Cells_Correlation_Overlap_DNA_RNA,-0.0018351295310575335 -Cells_Correlation_Overlap_ER_Mito,-0.0023477230225597383 -Cells_Correlation_Overlap_ER_RNA,-0.0015189203849550557 -Cells_Correlation_Overlap_Mito_RNA,-0.01090056861214169 -Cells_Correlation_RWC_AGP_DNA,-0.0005717426735926609 -Cells_Correlation_RWC_AGP_ER,-0.04370643120192696 -Cells_Correlation_RWC_AGP_Mito,-0.06838868796202577 -Cells_Correlation_RWC_AGP_RNA,-0.06512315194687308 -Cells_Correlation_RWC_DNA_AGP,0.03226085584858847 -Cells_Correlation_RWC_DNA_ER,0.011950104247496237 -Cells_Correlation_RWC_DNA_Mito,0.040364318418724524 -Cells_Correlation_RWC_DNA_RNA,0.008869470879025617 -Cells_Correlation_RWC_ER_AGP,0.020667730957766438 -Cells_Correlation_RWC_ER_DNA,0.016515990665116023 -Cells_Correlation_RWC_ER_Mito,-0.012910955457193792 -Cells_Correlation_RWC_ER_RNA,-0.0005929240240607255 -Cells_Correlation_RWC_Mito_AGP,0.031048539322536626 -Cells_Correlation_RWC_Mito_DNA,0.03640076571601908 -Cells_Correlation_RWC_Mito_ER,0.01745132333340789 -Cells_Correlation_RWC_Mito_RNA,0.005950674567058958 -Cells_Correlation_RWC_RNA_AGP,0.020386208686619653 -Cells_Correlation_RWC_RNA_DNA,0.01648919388181135 -Cells_Correlation_RWC_RNA_ER,0.0007936692306437882 -Cells_Correlation_RWC_RNA_Mito,-0.0054219350497947925 -Cells_Granularity_10_AGP,0.596234152294267 -Cells_Granularity_10_DNA,-0.7851998135894637 -Cells_Granularity_10_ER,1.006356986891573 -Cells_Granularity_10_Mito,0.5940764683506947 -Cells_Granularity_10_RNA,0.94420432138965 -Cells_Granularity_11_AGP,0.4138868934911998 -Cells_Granularity_11_DNA,-0.10800466408914808 -Cells_Granularity_11_ER,0.694851843444537 -Cells_Granularity_11_Mito,0.42958886224860204 -Cells_Granularity_11_RNA,0.8440434815802547 -Cells_Granularity_12_AGP,0.3918293759586837 -Cells_Granularity_12_DNA,0.012214930760951948 -Cells_Granularity_12_ER,0.308977406665583 -Cells_Granularity_12_Mito,0.26705617937029474 -Cells_Granularity_12_RNA,0.5465459982792902 -Cells_Granularity_13_AGP,0.2833969037609877 -Cells_Granularity_13_DNA,0.05823572888671964 -Cells_Granularity_13_ER,0.24010422929353953 -Cells_Granularity_13_Mito,0.13463010584683002 -Cells_Granularity_13_RNA,0.42771137309620805 -Cells_Granularity_14_AGP,0.13929858491397573 -Cells_Granularity_14_DNA,0.04592680658544454 -Cells_Granularity_14_ER,0.07506493549739646 -Cells_Granularity_14_Mito,0.12628788786606626 -Cells_Granularity_14_RNA,0.304561291016355 -Cells_Granularity_15_AGP,0.09791369283412432 -Cells_Granularity_15_DNA,0.03654871442464133 -Cells_Granularity_15_ER,-0.08895219720091548 -Cells_Granularity_15_Mito,0.047631719244600745 -Cells_Granularity_15_RNA,0.021043378629984233 -Cells_Granularity_16_AGP,0.22908158179492416 -Cells_Granularity_16_DNA,-0.008956050787458255 -Cells_Granularity_16_ER,-0.08525343482384294 -Cells_Granularity_16_Mito,0.043211702207216623 -Cells_Granularity_16_RNA,0.04083546293564159 -Cells_Granularity_1_AGP,-0.21710295542806524 -Cells_Granularity_1_DNA,1.3873379944710056 -Cells_Granularity_1_ER,0.1516085658906441 -Cells_Granularity_1_Mito,-3.0544891599066037 -Cells_Granularity_1_RNA,0.10180602949199002 -Cells_Granularity_2_AGP,-0.09516050069476396 -Cells_Granularity_2_DNA,0.07128963917784054 -Cells_Granularity_2_ER,-0.10608822266523679 -Cells_Granularity_2_Mito,-0.19870741917758614 -Cells_Granularity_2_RNA,0.0767041842324626 -Cells_Granularity_3_AGP,0.029085900487835374 -Cells_Granularity_3_DNA,0.25023543068349513 -Cells_Granularity_3_ER,-0.06595956053415278 -Cells_Granularity_3_Mito,-0.025962492107193758 -Cells_Granularity_3_RNA,0.07854730182568562 -Cells_Granularity_4_AGP,0.04153032185392205 -Cells_Granularity_4_DNA,0.3292410516775592 -Cells_Granularity_4_ER,-0.0027714574831714245 -Cells_Granularity_4_Mito,0.17574231526027695 -Cells_Granularity_4_RNA,0.10439082434010888 -Cells_Granularity_5_AGP,0.02413056826009213 -Cells_Granularity_5_DNA,0.5947706013259629 -Cells_Granularity_5_ER,0.09702724545083038 -Cells_Granularity_5_Mito,0.32818422977302136 -Cells_Granularity_5_RNA,0.17489218771772205 -Cells_Granularity_6_AGP,0.10047926114391956 -Cells_Granularity_6_DNA,1.3233054307858494 -Cells_Granularity_6_ER,0.28564498529284554 -Cells_Granularity_6_Mito,0.4407237864744503 -Cells_Granularity_6_RNA,0.3594213907907484 -Cells_Granularity_7_AGP,0.2572182577577159 -Cells_Granularity_7_DNA,1.0654463597319677 -Cells_Granularity_7_ER,0.584312094855339 -Cells_Granularity_7_Mito,0.5428594624265922 -Cells_Granularity_7_RNA,0.7287969169620432 -Cells_Granularity_8_AGP,0.48844090935903783 +Image_Texture_SumAverage_RNA_10_00_256,-1.0079076827872857 +Image_Texture_SumAverage_RNA_5_00_256,-1.0078189757788891 +Image_Texture_SumAverage_RNA_5_03_256,-1.0063272814160602 +Image_Texture_SumAverage_RNA_3_00_256,-1.0060191962692042 +Image_Texture_SumAverage_RNA_5_01_256,-1.0058143134749604 +Image_Texture_SumAverage_RNA_3_01_256,-1.0053217857809313 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+Cytoplasm_Texture_SumAverage_ER_5_03_256,-0.8869328059298698 +Cytoplasm_Texture_SumAverage_ER_10_00_256,-0.8855380724449198 +Cytoplasm_Texture_SumAverage_ER_5_00_256,-0.8851241166400685 +Cytoplasm_Texture_SumAverage_ER_5_01_256,-0.8819940379491582 +Cytoplasm_Texture_SumAverage_ER_3_03_256,-0.8793678692719882 Cells_Intensity_IntegratedIntensityEdge_ER,-0.8754152766367902 -Cells_Intensity_IntegratedIntensityEdge_Mito,-0.36255438695750775 +Cytoplasm_Texture_SumAverage_ER_3_02_256,-0.8739355822378392 +Cytoplasm_Texture_SumAverage_ER_3_01_256,-0.8716020360553887 +Cytoplasm_Texture_SumAverage_ER_10_02_256,-0.87159650781377 +Nuclei_Intensity_IntegratedIntensity_AGP,-0.8690322812162323 +Cytoplasm_Texture_SumAverage_ER_3_00_256,-0.8543839439431808 +Cytoplasm_AreaShape_EquivalentDiameter,-0.8395668528306656 +Cells_Texture_SumAverage_DNA_10_03_256,-0.827836993561583 +Image_ImageQuality_LocalFocusScore_OrigDNA_50,-0.821611657958909 +Nuclei_ObjectSkeleton_NumberTrunks_mito_skel,-0.797397897305308 +Cytoplasm_Texture_SumAverage_ER_10_01_256,-0.7952709453674724 +Cells_Granularity_10_DNA,-0.7851998135894637 +Cytoplasm_Texture_SumAverage_ER_10_03_256,-0.7762933737880879 +Cells_Texture_SumAverage_RNA_5_02_256,-0.7670706849902806 +Cytoplasm_Intensity_IntegratedIntensityEdge_AGP,-0.7666117426343475 +Cytoplasm_Texture_SumAverage_RNA_10_00_256,-0.7613886218052275 +Cells_Texture_SumAverage_RNA_3_02_256,-0.7602037993752504 +Cells_Texture_SumAverage_DNA_10_01_256,-0.753362689117012 +Cells_Texture_SumAverage_RNA_3_03_256,-0.7510382620129024 +Cells_Texture_SumAverage_RNA_3_01_256,-0.7469202470765794 +Cells_Texture_SumAverage_DNA_10_00_256,-0.7463006042296599 +Cytoplasm_Texture_SumAverage_RNA_5_03_256,-0.7363951657802996 +Cells_Texture_SumAverage_RNA_3_00_256,-0.7357326595444145 +Cytoplasm_Texture_SumAverage_RNA_10_02_256,-0.7347819214843301 +Cytoplasm_Texture_SumAverage_RNA_5_01_256,-0.7314751263495198 +Cells_Texture_SumAverage_RNA_5_00_256,-0.7280621897426685 +Cells_Texture_SumAverage_DNA_10_02_256,-0.7182838703198441 +Cytoplasm_Texture_SumAverage_RNA_10_01_256,-0.7042972259242863 +Cytoplasm_Texture_SumAverage_RNA_5_02_256,-0.6988952209386441 Cells_Intensity_IntegratedIntensityEdge_RNA,-0.6949187131877858 -Cells_Intensity_IntegratedIntensity_AGP,-8.726041726487477 -Cells_Intensity_IntegratedIntensity_DNA,-10.725060794402822 -Cells_Intensity_IntegratedIntensity_ER,-37.06701220315101 -Cells_Intensity_IntegratedIntensity_Mito,7.588884097822824 -Cells_Intensity_IntegratedIntensity_RNA,-16.6329745440802 -Cells_Intensity_LowerQuartileIntensity_AGP,0.00023427480097435393 -Cells_Intensity_LowerQuartileIntensity_DNA,-0.0005434652509588424 -Cells_Intensity_LowerQuartileIntensity_ER,-0.001785756317470256 -Cells_Intensity_LowerQuartileIntensity_Mito,0.0015181955092362686 -Cells_Intensity_LowerQuartileIntensity_RNA,-0.0013664957352809695 -Cells_Intensity_MADIntensity_AGP,0.000055494921779614234 -Cells_Intensity_MADIntensity_DNA,-0.00010842963993193421 -Cells_Intensity_MADIntensity_ER,-0.0022293182876528877 -Cells_Intensity_MADIntensity_Mito,0.00018594320813960895 -Cells_Intensity_MADIntensity_RNA,-0.0013091196236062956 -Cells_Intensity_MassDisplacement_AGP,-0.12612210649631395 -Cells_Intensity_MassDisplacement_DNA,-0.21229015040619936 -Cells_Intensity_MassDisplacement_ER,-0.0441582796957779 -Cells_Intensity_MassDisplacement_Mito,0.2624538579536116 -Cells_Intensity_MassDisplacement_RNA,0.0005424895629386195 -Cells_Intensity_MaxIntensityEdge_AGP,0.0009255150944924547 -Cells_Intensity_MaxIntensityEdge_DNA,-0.0005296738665539851 -Cells_Intensity_MaxIntensityEdge_ER,-0.004710120839155583 -Cells_Intensity_MaxIntensityEdge_Mito,-0.001404390787749983 -Cells_Intensity_MaxIntensityEdge_RNA,0.0016614083994914682 -Cells_Intensity_MaxIntensity_AGP,0.0076979904166707795 -Cells_Intensity_MaxIntensity_DNA,-0.00993267670890154 -Cells_Intensity_MaxIntensity_ER,0.008996949020780565 -Cells_Intensity_MaxIntensity_Mito,0.022803810663712912 -Cells_Intensity_MaxIntensity_RNA,0.00755704012333085 -Cells_Intensity_MeanIntensityEdge_AGP,-0.00015754685820115809 -Cells_Intensity_MeanIntensityEdge_DNA,-0.00037228832640053233 -Cells_Intensity_MeanIntensityEdge_ER,-0.0009260500451381411 -Cells_Intensity_MeanIntensityEdge_Mito,0.0013098442931704824 -Cells_Intensity_MeanIntensityEdge_RNA,-0.0006758525647754445 -Cells_Intensity_MeanIntensity_AGP,0.002163212314908001 -Cells_Intensity_MeanIntensity_DNA,-0.0009121621676226831 -Cells_Intensity_MeanIntensity_ER,-0.000928094681826008 -Cells_Intensity_MeanIntensity_Mito,0.004015021278321515 -Cells_Intensity_MeanIntensity_RNA,-0.0011625191958703233 -Cells_Intensity_MedianIntensity_AGP,0.000013807515621747134 -Cells_Intensity_MedianIntensity_DNA,-0.0007482708401769543 -Cells_Intensity_MedianIntensity_ER,-0.0034369679018983914 -Cells_Intensity_MedianIntensity_Mito,0.001548076214734232 -Cells_Intensity_MedianIntensity_RNA,-0.0029701507322425565 -Cells_Intensity_MinIntensityEdge_AGP,0.00011942252160394807 -Cells_Intensity_MinIntensityEdge_DNA,-0.0003326134238079297 -Cells_Intensity_MinIntensityEdge_ER,-0.0012415765409036323 -Cells_Intensity_MinIntensityEdge_Mito,0.0011585470151815865 -Cells_Intensity_MinIntensityEdge_RNA,-0.00043072441473440096 -Cells_Intensity_MinIntensity_AGP,0.00015985663719543714 -Cells_Intensity_MinIntensity_DNA,-0.0003440654005687455 -Cells_Intensity_MinIntensity_ER,-0.0012271140234978572 -Cells_Intensity_MinIntensity_Mito,0.0011605091192904613 -Cells_Intensity_MinIntensity_RNA,-0.000531460224846327 -Cells_Intensity_StdIntensityEdge_AGP,0.0005794793003774696 -Cells_Intensity_StdIntensityEdge_DNA,-0.000050477365047112437 -Cells_Intensity_StdIntensityEdge_ER,-0.00059168681738005 -Cells_Intensity_StdIntensityEdge_Mito,-0.0001580473553007118 -Cells_Intensity_StdIntensityEdge_RNA,0.0006575289564691067 -Cells_Intensity_StdIntensity_AGP,0.0018014693680225842 -Cells_Intensity_StdIntensity_DNA,-0.002488818674830493 -Cells_Intensity_StdIntensity_ER,0.002578715399341472 -Cells_Intensity_StdIntensity_Mito,0.00446932338714592 -Cells_Intensity_StdIntensity_RNA,0.0030285658444081246 -Cells_Intensity_UpperQuartileIntensity_AGP,0.0018318487169772806 -Cells_Intensity_UpperQuartileIntensity_DNA,-0.00024264974469195985 -Cells_Intensity_UpperQuartileIntensity_ER,-0.0024375655908164574 -Cells_Intensity_UpperQuartileIntensity_Mito,0.004297948285197734 -Cells_Intensity_UpperQuartileIntensity_RNA,-0.00031135897312522306 -Cells_Neighbors_AngleBetweenNeighbors_5,0.5360179230465688 -Cells_Neighbors_AngleBetweenNeighbors_Adjacent,0.5360179230465688 -Cells_Neighbors_FirstClosestDistance_5,1.0698115131865347 -Cells_Neighbors_FirstClosestDistance_Adjacent,1.0698115131865347 +Image_Granularity_1_Mito,-0.6898641282655307 +Cells_Texture_SumAverage_DNA_5_03_256,-0.6894226591684698 +Cells_Texture_SumAverage_RNA_5_03_256,-0.6838283509349175 +Cytoplasm_Texture_SumAverage_RNA_5_00_256,-0.6776639661534007 +Cytoplasm_Texture_SumAverage_RNA_10_03_256,-0.6769630047207387 +Nuclei_Neighbors_FirstClosestObjectNumber_1,-0.6705452486044208 +Cells_Texture_SumAverage_RNA_5_01_256,-0.6699931077216015 +Cytoplasm_Texture_SumAverage_RNA_3_03_256,-0.6590588999659062 +Cytoplasm_Texture_SumAverage_RNA_3_01_256,-0.6574207457142434 +Cells_Texture_SumAverage_DNA_5_01_256,-0.6541120635486527 +Cytoplasm_Intensity_IntegratedIntensityEdge_RNA,-0.6525718649339023 +Cytoplasm_Number_Object_Number,-0.6350213109314133 +Nuclei_Number_Object_Number,-0.635021273719006 +Cytoplasm_Intensity_IntegratedIntensityEdge_DNA,-0.6187770409157014 +Cells_Texture_SumAverage_DNA_5_02_256,-0.6172166155459297 +Cytoplasm_Texture_SumAverage_RNA_3_02_256,-0.6162284164101957 +Cells_Texture_SumAverage_DNA_5_00_256,-0.6102369004094313 +Image_Granularity_8_DNA,-0.6036394176102803 +Cytoplasm_Texture_SumAverage_RNA_3_00_256,-0.6019771765981337 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-Cytoplasm_Correlation_RWC_ER_Mito,-0.030304256671403847 -Cytoplasm_Correlation_RWC_ER_RNA,0.010091928479880906 -Cytoplasm_Correlation_RWC_Mito_AGP,0.02824641469049652 -Cytoplasm_Correlation_RWC_Mito_DNA,0.09099788891267169 -Cytoplasm_Correlation_RWC_Mito_ER,0.0006728117636032455 -Cytoplasm_Correlation_RWC_Mito_RNA,-0.0013120990714940405 -Cytoplasm_Correlation_RWC_RNA_AGP,0.006878467583065896 -Cytoplasm_Correlation_RWC_RNA_DNA,0.063011215992341 -Cytoplasm_Correlation_RWC_RNA_ER,-0.009598449966465385 -Cytoplasm_Correlation_RWC_RNA_Mito,-0.043666875484705894 -Cytoplasm_Granularity_10_AGP,0.3621773326485357 -Cytoplasm_Granularity_10_DNA,-0.20373435749552365 -Cytoplasm_Granularity_10_ER,0.8737197270652999 -Cytoplasm_Granularity_10_Mito,0.424656350801321 -Cytoplasm_Granularity_10_RNA,0.8512033420941886 -Cytoplasm_Granularity_11_AGP,0.29579689062696823 -Cytoplasm_Granularity_11_DNA,-0.0680622444862167 -Cytoplasm_Granularity_11_ER,0.6441981840495153 -Cytoplasm_Granularity_11_Mito,0.3372498149601655 -Cytoplasm_Granularity_11_RNA,0.8551610431381121 -Cytoplasm_Granularity_12_AGP,0.3764440058999277 -Cytoplasm_Granularity_12_DNA,-0.008023035035315346 -Cytoplasm_Granularity_12_ER,0.4083890593566485 -Cytoplasm_Granularity_12_Mito,0.2546074277567005 -Cytoplasm_Granularity_12_RNA,0.6815690660588413 -Cytoplasm_Granularity_13_AGP,0.2941404971215545 -Cytoplasm_Granularity_13_DNA,0.056252457697090294 -Cytoplasm_Granularity_13_ER,0.4041238385937302 -Cytoplasm_Granularity_13_Mito,0.13625058821626296 -Cytoplasm_Granularity_13_RNA,0.564982579033083 -Cytoplasm_Granularity_14_AGP,0.18810514909706852 -Cytoplasm_Granularity_14_DNA,0.05181143909090302 -Cytoplasm_Granularity_14_ER,0.15231812006586126 -Cytoplasm_Granularity_14_Mito,0.15968235797930422 -Cytoplasm_Granularity_14_RNA,0.4515304897087789 -Cytoplasm_Granularity_15_AGP,0.11607624300924287 -Cytoplasm_Granularity_15_DNA,0.06029073286687407 -Cytoplasm_Granularity_15_ER,-0.017196693300692928 -Cytoplasm_Granularity_15_Mito,0.08265224492550227 -Cytoplasm_Granularity_15_RNA,0.1258270286649022 -Cytoplasm_Granularity_16_AGP,0.25768207160923495 -Cytoplasm_Granularity_16_DNA,0.03531235954910723 -Cytoplasm_Granularity_16_ER,-0.05276527071166829 -Cytoplasm_Granularity_16_Mito,0.10519896695778408 -Cytoplasm_Granularity_16_RNA,0.06663556691439654 -Cytoplasm_Granularity_1_AGP,-0.16032819747438776 -Cytoplasm_Granularity_1_DNA,1.3638998136579663 -Cytoplasm_Granularity_1_ER,1.0734894716566492 -Cytoplasm_Granularity_1_Mito,-2.1366268821761536 -Cytoplasm_Granularity_1_RNA,0.8290323946636082 -Cytoplasm_Granularity_2_AGP,-0.09300142208414258 -Cytoplasm_Granularity_2_DNA,0.017515931736617057 -Cytoplasm_Granularity_2_ER,-0.08922429337417052 -Cytoplasm_Granularity_2_Mito,-0.3234770074409268 -Cytoplasm_Granularity_2_RNA,-0.011360637267031801 -Cytoplasm_Granularity_3_AGP,-0.06661711271720268 -Cytoplasm_Granularity_3_DNA,0.0032255606921311976 -Cytoplasm_Granularity_3_ER,-0.0009738794322228239 -Cytoplasm_Granularity_3_Mito,-0.15828057385212618 -Cytoplasm_Granularity_3_RNA,0.03134343568734323 -Cytoplasm_Granularity_4_AGP,-0.013221675007593914 -Cytoplasm_Granularity_4_DNA,0.015050296022387437 -Cytoplasm_Granularity_4_ER,0.06365231149741188 -Cytoplasm_Granularity_4_Mito,0.06512769524514161 -Cytoplasm_Granularity_4_RNA,0.05453937461903888 -Cytoplasm_Granularity_5_AGP,0.007665901018127891 -Cytoplasm_Granularity_5_DNA,-0.003560897289006805 -Cytoplasm_Granularity_5_ER,0.10459836416406235 -Cytoplasm_Granularity_5_Mito,0.2044082031068698 -Cytoplasm_Granularity_5_RNA,0.10234392110718821 -Cytoplasm_Granularity_6_AGP,0.027048843275872973 -Cytoplasm_Granularity_6_DNA,0.15423075808702702 -Cytoplasm_Granularity_6_ER,0.11055618357746361 -Cytoplasm_Granularity_6_Mito,0.25789512053979474 -Cytoplasm_Granularity_6_RNA,0.20742861466871815 -Cytoplasm_Granularity_7_AGP,0.02750084773422557 -Cytoplasm_Granularity_7_DNA,0.5310795606754721 -Cytoplasm_Granularity_7_ER,0.27645377239022684 -Cytoplasm_Granularity_7_Mito,0.32613349533567976 -Cytoplasm_Granularity_7_RNA,0.37651513436637396 -Cytoplasm_Granularity_8_AGP,0.1060362323508247 -Cytoplasm_Granularity_8_DNA,0.2273030963654442 -Cytoplasm_Granularity_8_ER,0.4815619433022671 -Cytoplasm_Granularity_8_Mito,0.5158686462872771 -Cytoplasm_Granularity_8_RNA,0.6357574216702501 -Cytoplasm_Granularity_9_AGP,0.29536540797916255 -Cytoplasm_Granularity_9_DNA,-0.28931632129251905 -Cytoplasm_Granularity_9_ER,0.6049647508883249 -Cytoplasm_Granularity_9_Mito,0.4923475167977173 -Cytoplasm_Granularity_9_RNA,0.8238675236184209 -Cytoplasm_Intensity_IntegratedIntensityEdge_AGP,-0.7666117426343475 -Cytoplasm_Intensity_IntegratedIntensityEdge_DNA,-0.6187770409157014 -Cytoplasm_Intensity_IntegratedIntensityEdge_ER,-1.381848570002734 -Cytoplasm_Intensity_IntegratedIntensityEdge_Mito,0.5267272432521739 -Cytoplasm_Intensity_IntegratedIntensityEdge_RNA,-0.6525718649339023 -Cytoplasm_Intensity_IntegratedIntensity_AGP,-13.605230616065032 -Cytoplasm_Intensity_IntegratedIntensity_DNA,-4.799600506759595 -Cytoplasm_Intensity_IntegratedIntensity_ER,-26.65422084451093 -Cytoplasm_Intensity_IntegratedIntensity_Mito,3.135042827922619 -Cytoplasm_Intensity_IntegratedIntensity_RNA,-15.984364000186112 -Cytoplasm_Intensity_LowerQuartileIntensity_AGP,0.0001739056705600719 -Cytoplasm_Intensity_LowerQuartileIntensity_DNA,-0.0005109913913806112 -Cytoplasm_Intensity_LowerQuartileIntensity_ER,-0.001447179504224655 -Cytoplasm_Intensity_LowerQuartileIntensity_Mito,0.0014677533200964867 -Cytoplasm_Intensity_LowerQuartileIntensity_RNA,-0.0012293040090376672 -Cytoplasm_Intensity_MADIntensity_AGP,0.00002563943617447023 -Cytoplasm_Intensity_MADIntensity_DNA,-0.00004522150507881915 -Cytoplasm_Intensity_MADIntensity_ER,-0.0017872135605927952 -Cytoplasm_Intensity_MADIntensity_Mito,0.00011733893509811101 -Cytoplasm_Intensity_MADIntensity_RNA,-0.0010369978210083516 -Cytoplasm_Intensity_MassDisplacement_AGP,-0.27592705054876354 -Cytoplasm_Intensity_MassDisplacement_DNA,-0.19796968546400587 -Cytoplasm_Intensity_MassDisplacement_ER,-0.07465026574570555 -Cytoplasm_Intensity_MassDisplacement_Mito,0.21865783056061994 -Cytoplasm_Intensity_MassDisplacement_RNA,-0.12353650214693196 -Cytoplasm_Intensity_MaxIntensityEdge_AGP,0.01052913326012946 -Cytoplasm_Intensity_MaxIntensityEdge_DNA,-0.001989515889089256 -Cytoplasm_Intensity_MaxIntensityEdge_ER,0.015665241157233008 -Cytoplasm_Intensity_MaxIntensityEdge_Mito,0.024281141574216132 -Cytoplasm_Intensity_MaxIntensityEdge_RNA,0.00971032444997739 -Cytoplasm_Intensity_MaxIntensity_AGP,0.007584473223878513 -Cytoplasm_Intensity_MaxIntensity_DNA,-0.0019817889846841224 -Cytoplasm_Intensity_MaxIntensity_ER,0.013913820228194606 -Cytoplasm_Intensity_MaxIntensity_Mito,0.022467622842449835 -Cytoplasm_Intensity_MaxIntensity_RNA,0.007865164538595027 -Cytoplasm_Intensity_MeanIntensityEdge_AGP,0.0023145496332955488 -Cytoplasm_Intensity_MeanIntensityEdge_DNA,0.0000668271630240606 -Cytoplasm_Intensity_MeanIntensityEdge_ER,0.0006395645589933456 -Cytoplasm_Intensity_MeanIntensityEdge_Mito,0.003784114416061897 -Cytoplasm_Intensity_MeanIntensityEdge_RNA,0.002216649088710337 -Cytoplasm_Intensity_MeanIntensity_AGP,0.0012701998019556066 -Cytoplasm_Intensity_MeanIntensity_DNA,-0.0007282669287627394 -Cytoplasm_Intensity_MeanIntensity_ER,-0.0014645907894204977 -Cytoplasm_Intensity_MeanIntensity_Mito,0.0029650681463885193 -Cytoplasm_Intensity_MeanIntensity_RNA,-0.0008345571187544096 -Cytoplasm_Intensity_MedianIntensity_AGP,0.00015858902929812338 -Cytoplasm_Intensity_MedianIntensity_DNA,-0.000646997007137674 -Cytoplasm_Intensity_MedianIntensity_ER,-0.003226614749310779 -Cytoplasm_Intensity_MedianIntensity_Mito,0.0016440346669668697 -Cytoplasm_Intensity_MedianIntensity_RNA,-0.0020827964538553496 -Cytoplasm_Intensity_MinIntensityEdge_AGP,0.00011944299938378014 -Cytoplasm_Intensity_MinIntensityEdge_DNA,-0.0003326134238079297 -Cytoplasm_Intensity_MinIntensityEdge_ER,-0.0012415640050637898 -Cytoplasm_Intensity_MinIntensityEdge_Mito,0.0011586143109303239 -Cytoplasm_Intensity_MinIntensityEdge_RNA,-0.00043068703401439386 -Cytoplasm_Intensity_MinIntensity_AGP,0.00016016008271078626 -Cytoplasm_Intensity_MinIntensity_DNA,-0.0003440654617230868 -Cytoplasm_Intensity_MinIntensity_ER,-0.0012272559460245799 -Cytoplasm_Intensity_MinIntensity_Mito,0.0011604648436654686 -Cytoplasm_Intensity_MinIntensity_RNA,-0.000531477314626829 -Cytoplasm_Intensity_StdIntensityEdge_AGP,0.002355461528566267 -Cytoplasm_Intensity_StdIntensityEdge_DNA,-0.00034775131178627766 -Cytoplasm_Intensity_StdIntensityEdge_ER,0.003662570730286337 -Cytoplasm_Intensity_StdIntensityEdge_Mito,0.005120232181651494 -Cytoplasm_Intensity_StdIntensityEdge_RNA,0.003596767364203492 -Cytoplasm_Intensity_StdIntensity_AGP,0.0011284258063885395 -Cytoplasm_Intensity_StdIntensity_DNA,-0.00030194202957900746 -Cytoplasm_Intensity_StdIntensity_ER,0.002139626416374333 -Cytoplasm_Intensity_StdIntensity_Mito,0.0037627009786817146 -Cytoplasm_Intensity_StdIntensity_RNA,0.0020296064916012647 -Cytoplasm_Intensity_UpperQuartileIntensity_AGP,0.0009447068256868544 -Cytoplasm_Intensity_UpperQuartileIntensity_DNA,-0.0007156594800842915 -Cytoplasm_Intensity_UpperQuartileIntensity_ER,-0.0034454003346373555 -Cytoplasm_Intensity_UpperQuartileIntensity_Mito,0.0018164595803681959 -Cytoplasm_Intensity_UpperQuartileIntensity_RNA,-0.0022273721391312877 -Cytoplasm_Number_Object_Number,-0.6350213109314133 -Cytoplasm_RadialDistribution_FracAtD_AGP_1of4,0.00029585683472527067 -Cytoplasm_RadialDistribution_FracAtD_AGP_2of4,0.0014983329804199937 -Cytoplasm_RadialDistribution_FracAtD_AGP_3of4,0.006769297958963709 -Cytoplasm_RadialDistribution_FracAtD_AGP_4of4,-0.008738726691518409 -Cytoplasm_RadialDistribution_FracAtD_DNA_1of4,0.0003031255679141364 -Cytoplasm_RadialDistribution_FracAtD_DNA_2of4,0.0016273798939790947 -Cytoplasm_RadialDistribution_FracAtD_DNA_3of4,0.007412401629733613 -Cytoplasm_RadialDistribution_FracAtD_DNA_4of4,-0.009347513076985642 -Cytoplasm_RadialDistribution_FracAtD_ER_1of4,0.00009018210403317813 -Cytoplasm_RadialDistribution_FracAtD_ER_2of4,0.0006776350654647571 -Cytoplasm_RadialDistribution_FracAtD_ER_3of4,0.005491824808924769 -Cytoplasm_RadialDistribution_FracAtD_ER_4of4,-0.0058056177955532785 -Cytoplasm_RadialDistribution_FracAtD_Mito_1of4,-0.00035605206569591874 -Cytoplasm_RadialDistribution_FracAtD_Mito_2of4,-0.00010260115904976095 -Cytoplasm_RadialDistribution_FracAtD_Mito_3of4,0.0038198800693511124 -Cytoplasm_RadialDistribution_FracAtD_Mito_4of4,-0.003380599130039947 -Cytoplasm_RadialDistribution_FracAtD_RNA_1of4,0.000412499534092029 -Cytoplasm_RadialDistribution_FracAtD_RNA_2of4,0.0019633220773082327 -Cytoplasm_RadialDistribution_FracAtD_RNA_3of4,0.006654764679618122 -Cytoplasm_RadialDistribution_FracAtD_RNA_4of4,-0.008944324428238115 -Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_10of16,0.001010360764099231 -Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_10of20,-3.3191860086745367e-7 -Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_11of16,0.017291244433720265 -Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_11of20,-4.107037610324741e-7 -Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_12of16,0.019148445047663276 -Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_12of20,0.00015594398035112723 -Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_13of16,-0.0024313806173598598 -Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_13of20,0.004740840647455922 -Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_14of16,-0.013477139123632988 -Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_14of20,0.01862016367722964 -Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_15of16,-0.005129333631243874 -Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_15of20,0.014075814221331613 -Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_16of16,-0.01158257019857007 -Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_16of20,-0.0008351334196381845 -Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_17of20,-0.010972222003400652 -Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_18of20,-0.011265593382448744 -Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_19of20,0.0037818099080025053 -Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_1of16,-1.2752611314838168e-7 -Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_1of20,-1.2752611516185063e-7 -Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_20of20,-0.011441275986973715 -Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_2of16,0.0 -Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_2of20,0.0 -Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_3of16,-1.8994544956484545e-12 -Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_3of20,0.0 -Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_4of16,-6.349563603402189e-10 -Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_4of20,1.0736991093969705e-10 -Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_5of16,2.0018344824933035e-8 -Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_5of20,-5.885879378656993e-10 -Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_6of16,6.777742891606627e-9 -Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_6of20,1.3000200984094511e-8 -Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_7of16,5.241366413601227e-8 -Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_7of20,4.1370415099564795e-9 -Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_8of16,-3.431114083009731e-7 -Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_8of20,8.56148585848497e-9 -Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_9of16,-1.7235538969388983e-6 -Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_9of20,3.323509957097867e-8 -Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_Overflow,0.0 -Cytoplasm_RadialDistribution_MeanFrac_AGP_1of4,-0.016833999370518497 -Cytoplasm_RadialDistribution_MeanFrac_AGP_2of4,-0.015219435602475444 -Cytoplasm_RadialDistribution_MeanFrac_AGP_3of4,-0.005397252262266053 -Cytoplasm_RadialDistribution_MeanFrac_AGP_4of4,0.0014808245100592092 -Cytoplasm_RadialDistribution_MeanFrac_DNA_1of4,-0.009835373795593284 -Cytoplasm_RadialDistribution_MeanFrac_DNA_2of4,-0.005496243550059515 -Cytoplasm_RadialDistribution_MeanFrac_DNA_3of4,0.0023150428179614917 -Cytoplasm_RadialDistribution_MeanFrac_DNA_4of4,0.004708125813373672 -Cytoplasm_RadialDistribution_MeanFrac_ER_1of4,-0.015814396334918105 -Cytoplasm_RadialDistribution_MeanFrac_ER_2of4,-0.007390293979286016 -Cytoplasm_RadialDistribution_MeanFrac_ER_3of4,-0.0032630781579524883 -Cytoplasm_RadialDistribution_MeanFrac_ER_4of4,-0.002653179680244359 -Cytoplasm_RadialDistribution_MeanFrac_Mito_1of4,-0.024299209968893086 -Cytoplasm_RadialDistribution_MeanFrac_Mito_2of4,-0.015764418414519435 -Cytoplasm_RadialDistribution_MeanFrac_Mito_3of4,0.007360644909382679 -Cytoplasm_RadialDistribution_MeanFrac_Mito_4of4,-0.00018161063658958683 -Cytoplasm_RadialDistribution_MeanFrac_RNA_1of4,-0.007139751285326862 -Cytoplasm_RadialDistribution_MeanFrac_RNA_2of4,-0.0038263381436397194 -Cytoplasm_RadialDistribution_MeanFrac_RNA_3of4,-0.00818675290704314 -Cytoplasm_RadialDistribution_MeanFrac_RNA_4of4,-0.002019095057263116 -Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_10of16,0.07902385373419045 -Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_10of20,-0.00011175088880755992 -Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_11of16,0.10350161119374701 -Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_11of20,0.0000868106102306053 -Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_12of16,-0.014147955823857663 -Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_12of20,0.022774453341023168 -Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_13of16,-0.0586822563910567 -Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_13of20,0.18211577976256657 -Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_14of16,-0.029089971458629835 -Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_14of20,0.0386303457022687 -Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_15of16,0.0338502242866419 -Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_15of20,-0.02225063372028952 -Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_16of16,0.01417707176271344 -Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_16of20,-0.0598487159201854 -Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_17of20,-0.02728432059976038 -Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_18of20,-0.019782223749178124 -Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_19of20,0.06975817407640802 -Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_1of16,-0.000021999168383374142 -Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_1of20,-0.000021999169070779787 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-Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_7of20,-0.00004719346175576327 -Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_8of16,-0.00010951397232600058 -Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_8of20,0.000010719510653467256 -Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_9of16,-0.00014269988526762248 -Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_9of20,0.00003358018820862765 -Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_Overflow,0.0 -Cytoplasm_RadialDistribution_RadialCV_AGP_1of4,0.00021383914805435215 -Cytoplasm_RadialDistribution_RadialCV_AGP_2of4,0.009958312614480113 -Cytoplasm_RadialDistribution_RadialCV_AGP_3of4,0.02091533097725352 -Cytoplasm_RadialDistribution_RadialCV_AGP_4of4,0.011756527891027303 -Cytoplasm_RadialDistribution_RadialCV_DNA_1of4,0.000487161826373427 -Cytoplasm_RadialDistribution_RadialCV_DNA_2of4,0.0034802660569412686 -Cytoplasm_RadialDistribution_RadialCV_DNA_3of4,0.008835894688703879 -Cytoplasm_RadialDistribution_RadialCV_DNA_4of4,-0.0005985991364623466 -Cytoplasm_RadialDistribution_RadialCV_ER_1of4,0.010208762236856201 -Cytoplasm_RadialDistribution_RadialCV_ER_2of4,0.02986378607516548 -Cytoplasm_RadialDistribution_RadialCV_ER_3of4,0.060045772748799325 -Cytoplasm_RadialDistribution_RadialCV_ER_4of4,0.033551666069542685 -Cytoplasm_RadialDistribution_RadialCV_Mito_1of4,-0.0011416973796075924 -Cytoplasm_RadialDistribution_RadialCV_Mito_2of4,0.019248056623892236 -Cytoplasm_RadialDistribution_RadialCV_Mito_3of4,0.05615514038325961 -Cytoplasm_RadialDistribution_RadialCV_Mito_4of4,0.028012372705169576 -Cytoplasm_RadialDistribution_RadialCV_RNA_1of4,0.02058967453966274 -Cytoplasm_RadialDistribution_RadialCV_RNA_2of4,0.05176237947723409 -Cytoplasm_RadialDistribution_RadialCV_RNA_3of4,0.08131654991013466 -Cytoplasm_RadialDistribution_RadialCV_RNA_4of4,0.03832157211530775 -Cytoplasm_RadialDistribution_RadialCV_mito_tubeness_10of16,0.03568811309490269 -Cytoplasm_RadialDistribution_RadialCV_mito_tubeness_10of20,-3.752723683990704e-7 -Cytoplasm_RadialDistribution_RadialCV_mito_tubeness_11of16,0.11339079426116876 -Cytoplasm_RadialDistribution_RadialCV_mito_tubeness_11of20,-0.00003637067257225454 -Cytoplasm_RadialDistribution_RadialCV_mito_tubeness_12of16,0.08193326085344255 -Cytoplasm_RadialDistribution_RadialCV_mito_tubeness_12of20,0.0038489139042725586 -Cytoplasm_RadialDistribution_RadialCV_mito_tubeness_13of16,0.07798887122461806 -Cytoplasm_RadialDistribution_RadialCV_mito_tubeness_13of20,0.09768311234877161 -Cytoplasm_RadialDistribution_RadialCV_mito_tubeness_14of16,0.05935437197201999 -Cytoplasm_RadialDistribution_RadialCV_mito_tubeness_14of20,0.10729890037951523 -Cytoplasm_RadialDistribution_RadialCV_mito_tubeness_15of16,0.03901986714698124 -Cytoplasm_RadialDistribution_RadialCV_mito_tubeness_15of20,0.0813978896901703 -Cytoplasm_RadialDistribution_RadialCV_mito_tubeness_16of16,0.027759520327881138 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-Nuclei_AreaShape_FormFactor,0.02654277896582402 -Nuclei_AreaShape_MajorAxisLength,-3.982642404817386 -Nuclei_AreaShape_MaxFeretDiameter,-3.9465948453074584 -Nuclei_AreaShape_MaximumRadius,-0.25259839420268776 -Nuclei_AreaShape_MeanRadius,-0.15325974675780807 -Nuclei_AreaShape_MedianRadius,-0.16312338287897069 -Nuclei_AreaShape_MinFeretDiameter,-0.5535906227967373 -Nuclei_AreaShape_MinorAxisLength,-0.46621386630105355 -Nuclei_AreaShape_Orientation,0.03094807747917555 -Nuclei_AreaShape_Perimeter,-8.674868186702492 -Nuclei_AreaShape_Solidity,0.0006917191170928796 -Nuclei_AreaShape_Zernike_0_0,0.04519866731984655 -Nuclei_AreaShape_Zernike_1_1,-0.00013616809226700902 -Nuclei_AreaShape_Zernike_2_0,-0.0072041193411901076 -Nuclei_AreaShape_Zernike_2_2,-0.0036616467677362827 -Nuclei_AreaShape_Zernike_3_1,-0.001677981296548822 -Nuclei_AreaShape_Zernike_3_3,0.00016520002281325807 -Nuclei_AreaShape_Zernike_4_0,0.004366402143550794 -Nuclei_AreaShape_Zernike_4_2,-0.0035391881847560403 -Nuclei_AreaShape_Zernike_4_4,-0.00323655220989844 -Nuclei_AreaShape_Zernike_5_1,0.0001297604635733921 -Nuclei_AreaShape_Zernike_5_3,0.0002451963176770061 -Nuclei_AreaShape_Zernike_5_5,2.1441070373470967e-6 -Nuclei_AreaShape_Zernike_6_0,0.0005993417402824289 -Nuclei_AreaShape_Zernike_6_2,0.00036130111183299134 -Nuclei_AreaShape_Zernike_6_4,0.0008202443485751942 -Nuclei_AreaShape_Zernike_6_6,-0.0017401927691897772 -Nuclei_AreaShape_Zernike_7_1,-0.00022026763482986984 +Cells_Intensity_MeanIntensityEdge_DNA,-0.00037228832640053233 +Cytoplasm_Texture_DifferenceVariance_RNA_3_02_256,-0.0003659446529737263 +Image_Intensity_LowerQuartileIntensity_ER__BackgroundOnly,-0.0003652464817976153 +Cytoplasm_Texture_DifferenceVariance_RNA_3_00_256,-0.00036189529381236885 +Nuclei_Texture_AngularSecondMoment_RNA_10_01_256,-0.000360207643429539 +Nuclei_Texture_AngularSecondMoment_RNA_10_03_256,-0.00035777353436451534 +Cytoplasm_RadialDistribution_FracAtD_Mito_1of4,-0.00035605206569591874 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+Image_Intensity_MedianIntensity_DNA_BackgroundOnly,-0.00029717421224569377 +Image_Intensity_LowerQuartileIntensity_DNA_BackgroundOnly,-0.0002878684369343576 +Cells_Texture_InfoMeas2_DNA_3_00_256,-0.00028176482356794593 +Cytoplasm_Texture_DifferenceVariance_RNA_3_03_256,-0.00027961092171189367 +Cytoplasm_Texture_DifferenceVariance_RNA_3_01_256,-0.0002702300676490023 +Cytoplasm_Texture_DifferenceVariance_RNA_5_00_256,-0.0002681336168467324 +Nuclei_Correlation_Manders_RNA_Mito,-0.00026151671428208383 +Nuclei_Texture_DifferenceVariance_RNA_3_02_256,-0.00025496954259964465 +Cytoplasm_Texture_DifferenceVariance_RNA_5_02_256,-0.00025072275725069844 +Image_Intensity_UpperQuartileIntensity_DNA_BackgroundOnly,-0.00024845548411296214 +Cytoplasm_Texture_DifferenceVariance_AGP_3_00_256,-0.0002474716708276796 +Nuclei_Texture_DifferenceVariance_RNA_3_00_256,-0.00024401015411574395 +Image_ImageQuality_MinIntensity_OrigAGP,-0.00024398805469600177 +Cytoplasm_Texture_InverseDifferenceMoment_AGP_3_00_256,-0.00024340926377099462 +Cells_Intensity_UpperQuartileIntensity_DNA,-0.00024264974469195985 Nuclei_AreaShape_Zernike_7_3,-0.00024171834706693084 -Nuclei_AreaShape_Zernike_7_5,0.000054844826719898 -Nuclei_AreaShape_Zernike_7_7,7.023865254600028e-7 +Cells_Texture_DifferenceVariance_RNA_3_02_256,-0.00023737693161533183 +Cells_AreaShape_Zernike_7_3,-0.00023686089860677935 +Nuclei_Correlation_Manders_ER_Mito,-0.00023482347164705203 +Cytoplasm_Texture_DifferenceVariance_RNA_5_03_256,-0.0002277086773898665 +Cells_Texture_DifferenceVariance_RNA_3_00_256,-0.00022445267613767405 +Cytoplasm_Texture_InverseDifferenceMoment_DNA_10_00_256,-0.0002218773525986626 +Image_ImageQuality_MADIntensity_OrigMito,-0.00022180537724499636 +Cytoplasm_Texture_DifferenceVariance_AGP_3_03_256,-0.00022123649069824124 +Nuclei_AreaShape_Zernike_7_1,-0.00022026763482986984 +Cells_AreaShape_Zernike_6_6,-0.00021677618508164333 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+Cytoplasm_RadialDistribution_MeanFrac_Mito_4of4,-0.00018161063658958683 +Cytoplasm_Texture_DifferenceVariance_AGP_5_03_256,-0.00017915657551808293 +Nuclei_Texture_DifferenceVariance_DNA_3_02_256,-0.000176953932390928 +Nuclei_Texture_DifferenceVariance_DNA_3_03_256,-0.00017607120375078954 +Cells_Texture_DifferenceVariance_RNA_5_00_256,-0.00017575449513391084 +Nuclei_Texture_DifferenceVariance_ER_3_03_256,-0.00017563699047612328 +Cells_Texture_DifferenceVariance_RNA_3_03_256,-0.00017532687934613102 +Nuclei_Texture_DifferenceVariance_DNA_3_00_256,-0.00017467154418711849 +Cytoplasm_Texture_DifferenceVariance_RNA_5_01_256,-0.00017443667601860094 +Nuclei_Texture_DifferenceVariance_DNA_5_02_256,-0.00016585825408888975 +Nuclei_Texture_DifferenceVariance_ER_5_00_256,-0.00016320441696996454 +Nuclei_Texture_DifferenceVariance_DNA_5_00_256,-0.0001631808897849694 +Cytoplasm_Texture_DifferenceVariance_RNA_10_00_256,-0.0001619074286383597 +Nuclei_Texture_DifferenceVariance_ER_3_01_256,-0.0001614993570718706 +Image_ImageQuality_MinIntensity_OrigER,-0.00016010878763472397 +Nuclei_Texture_DifferenceVariance_ER_5_01_256,-0.0001597509690994312 +Nuclei_Texture_DifferenceVariance_ER_5_02_256,-0.0001591274985287632 +Cells_Intensity_StdIntensityEdge_Mito,-0.0001580473553007118 +Cells_Intensity_MeanIntensityEdge_AGP,-0.00015754685820115809 +Nuclei_Texture_DifferenceVariance_DNA_5_03_256,-0.00015753461058918422 +Cytoplasm_Texture_DifferenceVariance_AGP_10_00_256,-0.000157140827679329 +Cytoplasm_Texture_DifferenceVariance_RNA_10_02_256,-0.0001544807765098223 +Nuclei_Texture_DifferenceVariance_DNA_3_01_256,-0.00015426311353878446 +Cells_Texture_DifferenceVariance_RNA_5_01_256,-0.00015404981611481067 +Nuclei_Texture_DifferenceVariance_ER_10_01_256,-0.00015396426846223273 Nuclei_AreaShape_Zernike_8_0,-0.00015340982529099374 +Nuclei_Correlation_Manders_AGP_RNA,-0.0001532510561808526 +Nuclei_Texture_DifferenceVariance_ER_10_02_256,-0.00015230835652600878 +Nuclei_Texture_DifferenceVariance_RNA_10_01_256,-0.00015217089964058455 +Image_Intensity_MinIntensity_DNA_BackgroundOnly,-0.0001515409468317288 +Cytoplasm_Texture_DifferenceVariance_AGP_10_02_256,-0.00014839663225870196 +Image_Threshold_FinalThreshold_CellsIncludingEdges,-0.0001481891162348804 +Nuclei_Texture_DifferenceVariance_RNA_10_03_256,-0.00014781416611511913 +Nuclei_Texture_DifferenceVariance_ER_10_03_256,-0.00014759418495997477 +Nuclei_Texture_DifferenceVariance_ER_5_03_256,-0.00014623978335265623 +Cells_Texture_DifferenceVariance_RNA_10_03_256,-0.00014439052805648931 +Cytoplasm_Texture_DifferenceVariance_RNA_10_01_256,-0.0001439184653015045 +Image_Intensity_MinIntensity_ER__BackgroundOnly,-0.00014364780491655266 +Image_Intensity_MinIntensity_ER,-0.00014364725094172554 +Cytoplasm_AreaShape_Zernike_7_3,-0.0001436045187172707 +Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_9of16,-0.00014269988526762248 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+Cells_AreaShape_Zernike_9_5,-0.00012491445029434644 +Cells_Texture_DifferenceVariance_RNA_10_01_256,-0.00012434337052254305 +Cytoplasm_Texture_DifferenceVariance_AGP_5_01_256,-0.0001204714304394377 +Cells_AreaShape_Zernike_6_0,-0.00011874938475083744 +Nuclei_Texture_DifferenceVariance_DNA_10_01_256,-0.00011383615062887496 +Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_10of20,-0.00011175088880755992 +Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_8of16,-0.00010951397232600058 +Cells_Intensity_MADIntensity_DNA,-0.00010842963993193421 +Image_Intensity_MeanIntensity_AGP,-0.00010688894888608383 +Cells_AreaShape_Zernike_8_4,-0.00010646330397565297 Nuclei_AreaShape_Zernike_8_2,-0.0001033759565893682 -Nuclei_AreaShape_Zernike_8_4,-0.0006182035080634972 -Nuclei_AreaShape_Zernike_8_6,0.00035854849512647957 -Nuclei_AreaShape_Zernike_8_8,-0.0008542139542975037 +Cytoplasm_RadialDistribution_FracAtD_Mito_2of4,-0.00010260115904976095 +Nuclei_Correlation_Manders_Mito_RNA,-0.0001020064306317593 +Nuclei_Texture_DifferenceVariance_DNA_10_03_256,-0.00009667054121501213 +Image_Texture_AngularSecondMoment_ER_5_02_256,-0.00009379226716473357 +Nuclei_Intensity_StdIntensityEdge_DNA,-0.00008135364626326951 +Cytoplasm_Texture_DifferenceEntropy_DNA_10_00_256,-0.00007896990400543813 +Image_Texture_DifferenceVariance_AGP_10_03_256,-0.00007089993821664871 +Image_Intensity_MADIntensity_AGP,-0.00006222002646433657 +Cytoplasm_Correlation_Overlap_DNA_ER,-0.000057749405112918206 Nuclei_AreaShape_Zernike_9_1,-0.00005676529822341755 -Nuclei_AreaShape_Zernike_9_3,-0.000022697865555455817 +Image_Intensity_PercentMaximal_DNA_BackgroundOnly,-0.00005529156205966465 +Image_Intensity_MeanIntensity_RNA_BackgroundOnly,-0.000053167620224647805 +Cells_Intensity_StdIntensityEdge_DNA,-0.000050477365047112437 +Image_Texture_DifferenceVariance_AGP_10_01_256,-0.00004932017187333961 +Image_Intensity_PercentMaximal_RNA_BackgroundOnly,-0.000049018585561526565 +Image_Intensity_PercentMaximal_ER__BackgroundOnly,-0.000048946260528968206 +Image_Intensity_PercentMaximal_AGP__BackgroundOnly,-0.00004853455634760872 +Image_Intensity_PercentMaximal_Mito_BackgroundOnly,-0.000048169944938310086 +Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_7of20,-0.00004719346175576327 Nuclei_AreaShape_Zernike_9_5,-0.00004606985000127373 +Cells_AreaShape_Zernike_8_2,-0.00004535315999088664 +Cytoplasm_Intensity_MADIntensity_DNA,-0.00004522150507881915 +Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_6of16,-0.000044947613595566 +Image_Intensity_MinIntensity_AGP,-0.00004390991245549228 +Image_Intensity_MinIntensity_AGP__BackgroundOnly,-0.00004374414587294325 Nuclei_AreaShape_Zernike_9_7,-0.00004299538401916059 -Nuclei_AreaShape_Zernike_9_9,0.00003730039309079869 +Cells_AreaShape_Zernike_9_3,-0.00004290185843275918 +Image_Texture_DifferenceVariance_AGP_10_02_256,-0.000041902910829404 +Nuclei_Correlation_Manders_DNA_ER,-0.000041755952428722995 +Cytoplasm_RadialDistribution_RadialCV_mito_tubeness_11of20,-0.00003637067257225454 +Cytoplasm_RadialDistribution_RadialCV_mito_tubeness_9of16,-0.00002380494635400272 +Nuclei_AreaShape_Zernike_9_3,-0.000022697865555455817 +Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_1of20,-0.000021999169070779787 +Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_1of16,-0.000021999168383374142 +Image_Intensity_MADIntensity_Mito_BackgroundOnly,-0.000019659048495312346 +Cytoplasm_RadialDistribution_RadialCV_mito_tubeness_9of20,-0.000017542469105890698 +Image_Texture_DifferenceVariance_AGP_10_00_256,-0.000017383915707880337 +Cytoplasm_RadialDistribution_RadialCV_mito_tubeness_6of16,-0.00001629819404393165 +Nuclei_Correlation_Manders_RNA_ER,-0.00001613229172694085 +Cells_Texture_DifferenceVariance_ER_10_03_256,-0.00001413433285362125 +Image_ImageQuality_PercentMinimal_OrigDNA,-8.827235433273017e-6 +Image_Texture_DifferenceVariance_AGP_5_03_256,-8.27035008339771e-6 +Nuclei_Correlation_Manders_Mito_ER,-8.254205686724183e-6 +Cytoplasm_RadialDistribution_RadialCV_mito_tubeness_6of20,-7.234115593514523e-6 +Cytoplasm_RadialDistribution_RadialCV_mito_tubeness_4of16,-6.608223723049194e-6 +Cytoplasm_RadialDistribution_RadialCV_mito_tubeness_8of16,-5.925223649304366e-6 +Cytoplasm_RadialDistribution_RadialCV_mito_tubeness_5of20,-5.808435716964275e-6 +Cytoplasm_RadialDistribution_RadialCV_mito_tubeness_7of16,-5.305520466840691e-6 +Cytoplasm_RadialDistribution_RadialCV_mito_tubeness_5of16,-4.771599377077925e-6 +Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_5of20,-3.129766114066346e-6 +Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_4of16,-2.5549418585753894e-6 +Cells_AreaShape_Zernike_7_1,-2.43639351642499e-6 +Image_Intensity_PercentMaximal_ER,-1.9483007032519577e-6 +Cells_Children_Cytoplasm_Count,-1.7881393432617188e-6 Nuclei_Children_Cytoplasm_Count,-1.7881393432617188e-6 -Nuclei_Correlation_Correlation_AGP_DNA,0.11722584730890678 -Nuclei_Correlation_Correlation_AGP_ER,0.0961629016304256 -Nuclei_Correlation_Correlation_AGP_Mito,0.05607547022761732 -Nuclei_Correlation_Correlation_AGP_RNA,0.09917653154575096 -Nuclei_Correlation_Correlation_DNA_ER,-0.01574560525143743 -Nuclei_Correlation_Correlation_DNA_Mito,-0.005222337165906046 -Nuclei_Correlation_Correlation_DNA_RNA,0.007971551708494723 -Nuclei_Correlation_Correlation_ER_Mito,0.050336011232411576 -Nuclei_Correlation_Correlation_ER_RNA,-0.01085611009652546 -Nuclei_Correlation_Correlation_Mito_RNA,0.026433648518168325 -Nuclei_Correlation_K_AGP_DNA,-0.26030202034391275 -Nuclei_Correlation_K_AGP_ER,-0.12476235367482649 -Nuclei_Correlation_K_AGP_Mito,0.03968826984291805 -Nuclei_Correlation_K_AGP_RNA,0.044667411935504026 -Nuclei_Correlation_K_DNA_AGP,0.08714339064500487 -Nuclei_Correlation_K_DNA_ER,0.005554164445932264 -Nuclei_Correlation_K_DNA_Mito,-0.03592229521023549 -Nuclei_Correlation_K_DNA_RNA,0.09333550669084491 -Nuclei_Correlation_K_ER_AGP,0.04097804575867828 -Nuclei_Correlation_K_ER_DNA,-0.004433008069688604 -Nuclei_Correlation_K_ER_Mito,0.0309586851156414 -Nuclei_Correlation_K_ER_RNA,0.044307805985728935 -Nuclei_Correlation_K_Mito_AGP,-0.08155926201339228 -Nuclei_Correlation_K_Mito_DNA,-0.06004734519578653 -Nuclei_Correlation_K_Mito_ER,-0.07843150997780383 -Nuclei_Correlation_K_Mito_RNA,-0.09321219444889922 -Nuclei_Correlation_K_RNA_AGP,-0.016388672414542352 -Nuclei_Correlation_K_RNA_DNA,-0.08698177642750818 -Nuclei_Correlation_K_RNA_ER,-0.026355498927584514 -Nuclei_Correlation_K_RNA_Mito,0.010281250302509003 -Nuclei_Correlation_Manders_AGP_DNA,-0.005152673022789054 +Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_9of16,-1.7235538969388983e-6 +Image_Intensity_MADIntensity_DNA_BackgroundOnly,-1.1410174368077093e-6 +Image_ImageQuality_PercentMinimal_OrigMito,-1.0094590834341943e-6 +Image_Intensity_PercentMaximal_DNA,-8.304195944219828e-7 +Image_ImageQuality_PercentMinimal_OrigAGP,-8.199931471608579e-7 +Image_ImageQuality_PercentMinimal_OrigRNA,-8.095157681964338e-7 +Image_ImageQuality_PercentMinimal_OrigER,-5.613583198282868e-7 +Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_11of20,-4.107037610324741e-7 +Cytoplasm_RadialDistribution_RadialCV_mito_tubeness_10of20,-3.752723683990704e-7 +Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_8of16,-3.431114083009731e-7 +Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_10of20,-3.3191860086745367e-7 +Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_1of20,-1.2752611516185063e-7 +Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_1of16,-1.2752611314838168e-7 +Cytoplasm_RadialDistribution_RadialCV_mito_tubeness_8of20,-3.8279285222573415e-8 +Image_Intensity_PercentMaximal_AGP,-3.727836883626878e-8 +Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_4of16,-6.349563603402189e-10 +Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_5of20,-5.885879378656993e-10 +Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_3of16,-1.8994544956484545e-12 +Cells_RadialDistribution_FracAtD_mito_tubeness_Overflow,0.0 +Cells_RadialDistribution_MeanFrac_mito_tubeness_Overflow,0.0 +Cells_RadialDistribution_RadialCV_mito_tubeness_Overflow,0.0 +Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_2of16,0.0 +Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_2of20,0.0 +Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_3of20,0.0 +Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_Overflow,0.0 +Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_2of16,0.0 +Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_2of20,0.0 +Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_3of20,0.0 +Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_Overflow,0.0 +Cytoplasm_RadialDistribution_RadialCV_mito_tubeness_1of16,0.0 +Cytoplasm_RadialDistribution_RadialCV_mito_tubeness_1of20,0.0 +Cytoplasm_RadialDistribution_RadialCV_mito_tubeness_2of16,0.0 +Cytoplasm_RadialDistribution_RadialCV_mito_tubeness_2of20,0.0 +Cytoplasm_RadialDistribution_RadialCV_mito_tubeness_3of20,0.0 +Cytoplasm_RadialDistribution_RadialCV_mito_tubeness_Overflow,0.0 +Image_ImageQuality_Scaling_OrigAGP,0.0 +Image_ImageQuality_Scaling_OrigDNA,0.0 +Image_ImageQuality_Scaling_OrigER,0.0 +Image_ImageQuality_Scaling_OrigMito,0.0 +Image_ImageQuality_Scaling_OrigRNA,0.0 +Image_ImageQuality_TotalArea_OrigAGP,0.0 +Image_ImageQuality_TotalArea_OrigDNA,0.0 +Image_ImageQuality_TotalArea_OrigER,0.0 +Image_ImageQuality_TotalArea_OrigMito,0.0 +Image_ImageQuality_TotalArea_OrigRNA,0.0 +Image_Intensity_TotalArea_AGP,0.0 +Image_Intensity_TotalArea_DNA,0.0 +Image_Intensity_TotalArea_ER,0.0 +Image_Intensity_TotalArea_Mito,0.0 +Image_Intensity_TotalArea_RNA,0.0 +Nuclei_Location_CenterMassIntensity_Z_AGP,0.0 +Nuclei_Location_CenterMassIntensity_Z_DNA,0.0 +Nuclei_Location_CenterMassIntensity_Z_ER,0.0 +Nuclei_Location_CenterMassIntensity_Z_Mito,0.0 +Nuclei_Location_CenterMassIntensity_Z_RNA,0.0 +Nuclei_Location_Center_Z,0.0 +Nuclei_Location_MaxIntensity_Z_AGP,0.0 +Nuclei_Location_MaxIntensity_Z_DNA,0.0 +Nuclei_Location_MaxIntensity_Z_ER,0.0 +Nuclei_Location_MaxIntensity_Z_Mito,0.0 +Nuclei_Location_MaxIntensity_Z_RNA,0.0 +Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_4of20,1.0736991093969705e-10 +Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_3of16,3.910468735090281e-10 +Cytoplasm_RadialDistribution_RadialCV_mito_tubeness_3of16,1.8563064976312594e-9 +Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_7of20,4.1370415099564795e-9 +Cytoplasm_RadialDistribution_RadialCV_mito_tubeness_4of20,4.884752709781112e-9 +Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_6of16,6.777742891606627e-9 +Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_8of20,8.56148585848497e-9 +Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_6of20,1.3000200984094511e-8 +Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_5of16,2.0018344824933035e-8 +Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_9of20,3.323509957097867e-8 +Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_7of16,5.241366413601227e-8 +Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_4of20,1.3174989509683894e-7 +Cytoplasm_RadialDistribution_RadialCV_mito_tubeness_7of20,1.7631197557704483e-7 +Image_Intensity_PercentMaximal_Mito,5.174034213708555e-7 +Nuclei_AreaShape_Zernike_7_7,7.023865254600028e-7 +Image_Intensity_PercentMaximal_RNA,1.6101816401421145e-6 +Nuclei_AreaShape_Zernike_5_5,2.1441070373470967e-6 +Cytoplasm_AreaShape_Zernike_9_3,2.9439776307675546e-6 +Cells_Texture_DifferenceVariance_ER_10_01_256,5.1736117391289974e-6 +Nuclei_RadialDistribution_FracAtD_ER_3of4,6.853301556507003e-6 +Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_8of20,0.000010719510653467256 +Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_6of20,0.000011767505821188609 +Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_5of16,0.000012585603891228286 Nuclei_Correlation_Manders_AGP_ER,0.00001269256056365537 -Nuclei_Correlation_Manders_AGP_Mito,-0.0009050734034874975 -Nuclei_Correlation_Manders_AGP_RNA,-0.0001532510561808526 +Cells_Intensity_MedianIntensity_AGP,0.000013807515621747134 +Cells_AreaShape_Zernike_9_1,0.000015534182828478645 +Cytoplasm_Intensity_MADIntensity_AGP,0.00002563943617447023 +Nuclei_Intensity_MADIntensity_DNA,0.000028147863058188352 +Image_ImageQuality_PercentMaximal_OrigDNA,0.00003138216486150741 +Image_Texture_DifferenceVariance_AGP_5_01_256,0.00003246246633120014 +Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_9of20,0.00003358018820862765 +Nuclei_AreaShape_Zernike_9_9,0.00003730039309079869 +Cells_AreaShape_Zernike_8_0,0.00004586882265528156 +Image_ImageQuality_PercentMaximal_OrigAGP,0.00004945143414511413 +Cytoplasm_Texture_DifferenceVariance_ER_10_03_256,0.00004975380459252589 +Cells_Texture_DifferenceVariance_ER_10_02_256,0.00005013108333675213 +Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_7of16,0.00005078892801795663 +Cells_Texture_DifferenceVariance_ER_10_00_256,0.00005135709601314462 +Nuclei_AreaShape_Zernike_7_5,0.000054844826719898 +Image_Texture_DifferenceVariance_AGP_5_02_256,0.00005539833111107846 +Cells_Intensity_MADIntensity_AGP,0.000055494921779614234 +Image_Texture_DifferenceVariance_AGP_3_03_256,0.0000598756983965881 +Cells_Texture_DifferenceVariance_ER_5_03_256,0.00006420385187167637 +Cytoplasm_Intensity_MeanIntensityEdge_DNA,0.0000668271630240606 +Cells_AreaShape_Zernike_7_7,0.00006819751242127529 +Image_Texture_DifferenceVariance_AGP_5_00_256,0.0000694281749515352 +Nuclei_Texture_AngularSecondMoment_DNA_10_03_256,0.00007068427450281078 +Cells_Texture_DifferenceVariance_ER_5_01_256,0.00007524427538039592 +Cytoplasm_AreaShape_Zernike_7_7,0.00007583954681977738 +Image_Intensity_MADIntensity_ER__BackgroundOnly,0.0000771105121079988 +Cytoplasm_Texture_DifferenceVariance_ER_3_00_256,0.00007761928379764393 +Cells_Correlation_Manders_DNA_RNA,0.00007801378551210505 +Cytoplasm_Texture_InverseDifferenceMoment_AGP_5_01_256,0.00007842988583502825 +Cytoplasm_AreaShape_Zernike_9_9,0.0000796883177690307 +Cytoplasm_Texture_DifferenceVariance_ER_10_01_256,0.00008062101781047002 +Cells_AreaShape_Zernike_6_2,0.00008143226739556897 +Cytoplasm_Texture_DifferenceVariance_ER_3_02_256,0.0000832617025691468 +Image_ImageQuality_MinIntensity_OrigRNA,0.00008376706672874964 +Cytoplasm_RadialDistribution_MeanFrac_mito_tubeness_11of20,0.0000868106102306053 +Cytoplasm_RadialDistribution_FracAtD_ER_1of4,0.00009018210403317813 +Image_Intensity_LowerQuartileIntensity_RNA_BackgroundOnly,0.00009067336640944477 +Cells_AreaShape_Zernike_9_9,0.00009160369925461604 +Cells_Texture_DifferenceVariance_ER_3_03_256,0.00009796594009955223 +Image_Texture_DifferenceVariance_AGP_3_02_256,0.00009819463611128345 +Image_Texture_DifferenceVariance_AGP_3_01_256,0.00009992537241312145 +Image_ImageQuality_PercentMaximal_OrigER,0.0001013650021978443 +Cells_Texture_AngularSecondMoment_AGP_10_01_256,0.00010142309654155753 +Cytoplasm_Texture_DifferenceVariance_ER_3_03_256,0.00010495945422589166 +Cells_AreaShape_Zernike_5_5,0.00010853350162064055 +Cells_Texture_DifferenceVariance_ER_3_01_256,0.0001096259061771803 +Cells_Texture_DifferenceVariance_ER_5_00_256,0.00010976340145341362 +Cells_Texture_DifferenceVariance_ER_5_02_256,0.00011571842369444314 +Cytoplasm_Intensity_MADIntensity_Mito,0.00011733893509811101 +Image_Texture_DifferenceVariance_AGP_3_00_256,0.0001184154150342513 +Cytoplasm_Texture_DifferenceVariance_ER_5_03_256,0.00011874290420787852 +Cytoplasm_Texture_DifferenceVariance_ER_10_02_256,0.00011885658651956759 +Cells_Intensity_MinIntensityEdge_AGP,0.00011942252160394807 +Cytoplasm_Intensity_MinIntensityEdge_AGP,0.00011944299938378014 +Cells_Texture_DifferenceVariance_ER_3_00_256,0.00012383535990408015 +Cells_Correlation_Manders_DNA_ER,0.00012595801155175165 +Image_Intensity_MinIntensity_RNA,0.00012675745594816428 +Image_Intensity_MinIntensity_RNA_BackgroundOnly,0.00012675770626241684 +Image_Intensity_StdIntensity_DNA_BackgroundOnly,0.00012729132001082345 +Nuclei_AreaShape_Zernike_5_1,0.0001297604635733921 +Cytoplasm_Texture_DifferenceVariance_ER_5_02_256,0.0001321442500152475 +Cytoplasm_Texture_DifferenceVariance_ER_5_01_256,0.0001323748985364436 +Cells_Texture_DifferenceVariance_ER_3_02_256,0.00013241570325246439 +Cytoplasm_Texture_DifferenceVariance_ER_5_00_256,0.00013453088001481308 +Cytoplasm_Texture_DifferenceVariance_ER_3_01_256,0.0001348376259113302 +Cytoplasm_Texture_DifferenceVariance_ER_10_00_256,0.00013499454882557856 +Image_Intensity_MADIntensity_AGP__BackgroundOnly,0.00013742448156574277 +Image_Intensity_MedianIntensity_RNA_BackgroundOnly,0.00013870562258492928 +Image_Intensity_StdIntensity_Mito_BackgroundOnly,0.00014561608429774234 +Image_Intensity_MADIntensity_RNA_BackgroundOnly,0.00015166459757819459 +Image_Texture_DifferenceVariance_RNA_10_03_256,0.00015564548164527444 +Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_12of20,0.00015594398035112723 +Image_Texture_DifferenceVariance_RNA_10_01_256,0.00015777960611075185 +Cytoplasm_Intensity_MedianIntensity_AGP,0.00015858902929812338 +Cells_Intensity_MinIntensity_AGP,0.00015985663719543714 +Cytoplasm_Intensity_MinIntensity_AGP,0.00016016008271078626 +Cytoplasm_AreaShape_Zernike_5_5,0.00016128687126500314 +Nuclei_AreaShape_Zernike_3_3,0.00016520002281325807 +Cytoplasm_Intensity_LowerQuartileIntensity_AGP,0.0001739056705600719 +Cells_Intensity_MADIntensity_Mito,0.00018594320813960895 +Image_Intensity_LowerQuartileIntensity_AGP__BackgroundOnly,0.00020129741568991936 +Image_Texture_DifferenceVariance_RNA_10_02_256,0.00020690351501833932 +Cells_RadialDistribution_FracAtD_mito_tubeness_1of20,0.00020984840140846146 +Cytoplasm_RadialDistribution_RadialCV_AGP_1of4,0.00021383914805435215 +Image_Intensity_MinIntensity_Mito_BackgroundOnly,0.00021498583654121865 +Nuclei_Correlation_Manders_DNA_Mito,0.00022109195072310753 +Image_Intensity_MinIntensity_Mito,0.00022404685614431445 +Image_Texture_DifferenceVariance_RNA_10_00_256,0.00022540075050991762 +Cells_Intensity_LowerQuartileIntensity_AGP,0.00023427480097435393 +Image_Intensity_UpperQuartileIntensity_Mito,0.00023565540236479673 +Nuclei_Correlation_Overlap_AGP_ER,0.0002359578101966504 +Cells_Texture_AngularSecondMoment_AGP_10_00_256,0.00023778237064180866 +Cytoplasm_Texture_Correlation_Mito_10_02_256,0.00024152310152896796 +Image_Texture_DifferenceVariance_RNA_5_01_256,0.00024331348294199617 +Nuclei_AreaShape_Zernike_5_3,0.0002451963176770061 +Image_Intensity_MeanIntensity_AGP__BackgroundOnly,0.0002505854945480398 +Cells_AreaShape_Zernike_3_3,0.0002537529578712513 +Image_Texture_DifferenceVariance_RNA_5_03_256,0.000253872298360883 +Image_Texture_DifferenceVariance_RNA_3_03_256,0.0002547519699708985 +Nuclei_Correlation_RWC_DNA_RNA,0.00025768550336341834 +Image_Texture_DifferenceVariance_RNA_5_02_256,0.0002583328151993362 +Image_Texture_DifferenceVariance_DNA_3_02_256,0.00026504851994637116 +Image_Texture_DifferenceVariance_RNA_5_00_256,0.00026828837918511824 +Image_Texture_DifferenceVariance_RNA_3_01_256,0.00027101325002219074 +Image_Texture_DifferenceVariance_RNA_3_02_256,0.0002742208272266581 +Image_Texture_DifferenceVariance_RNA_3_00_256,0.0002780396773705902 +Image_Texture_DifferenceVariance_DNA_3_00_256,0.0002787655808869099 +Cytoplasm_Texture_InverseDifferenceMoment_AGP_5_02_256,0.0002827546465801051 +Cytoplasm_RadialDistribution_FracAtD_AGP_1of4,0.00029585683472527067 +Cytoplasm_RadialDistribution_FracAtD_DNA_1of4,0.0003031255679141364 +Cytoplasm_AreaShape_Zernike_9_1,0.00031400233857506503 +Image_Texture_DifferenceVariance_DNA_3_03_256,0.0003141096643736269 +Image_Texture_DifferenceVariance_DNA_3_01_256,0.00031603507820061026 +Image_Intensity_StdIntensity_AGP__BackgroundOnly,0.00032684810819878877 +Image_Texture_DifferenceVariance_ER_10_03_256,0.00033143652071377424 +Image_Texture_DifferenceVariance_ER_10_01_256,0.00033902979130223524 +Image_Texture_DifferenceVariance_DNA_5_02_256,0.00034078039672730113 +Cells_RadialDistribution_FracAtD_mito_tubeness_1of16,0.0003446008096504843 +Image_Texture_DifferenceVariance_DNA_5_00_256,0.00035340833545521074 +Image_ImageQuality_MinIntensity_OrigMito,0.00035511902764060393 +Image_Texture_AngularSecondMoment_ER_3_03_256,0.00035635039010940145 +Nuclei_AreaShape_Zernike_8_6,0.00035854849512647957 +Nuclei_AreaShape_Zernike_6_2,0.00036130111183299134 +Cytoplasm_Correlation_Overlap_ER_Mito,0.0003621220153556759 +Image_Texture_AngularSecondMoment_ER_5_00_256,0.00036728374291169694 +Image_Texture_DifferenceVariance_ER_10_00_256,0.00037100488028894013 +Image_Texture_DifferenceVariance_Mito_10_01_256,0.00038600700735090163 +Image_Texture_DifferenceVariance_Mito_3_02_256,0.0003860415397086 +Image_Texture_DifferenceVariance_ER_10_02_256,0.0003928834589171414 +Image_Texture_DifferenceVariance_Mito_3_00_256,0.0003986131967625244 +Image_Texture_DifferenceVariance_Mito_10_03_256,0.00040244105790090425 +Nuclei_RadialDistribution_FracAtD_RNA_3of4,0.00040409527568573903 Nuclei_Correlation_Manders_DNA_AGP,0.00040430542702499115 -Nuclei_Correlation_Manders_DNA_ER,-0.000041755952428722995 -Nuclei_Correlation_Manders_DNA_Mito,0.00022109195072310753 -Nuclei_Correlation_Manders_DNA_RNA,-0.00013670304799854392 -Nuclei_Correlation_Manders_ER_AGP,0.0004920489612936107 -Nuclei_Correlation_Manders_ER_DNA,-0.004597860013412841 -Nuclei_Correlation_Manders_ER_Mito,-0.00023482347164705203 -Nuclei_Correlation_Manders_ER_RNA,-0.00014028975428751243 -Nuclei_Correlation_Manders_Mito_AGP,0.00046997274308051154 -Nuclei_Correlation_Manders_Mito_DNA,-0.006216139011601871 -Nuclei_Correlation_Manders_Mito_ER,-8.254205686724183e-6 -Nuclei_Correlation_Manders_Mito_RNA,-0.0001020064306317593 +Image_Texture_DifferenceVariance_ER_5_03_256,0.000404898982759377 +Image_Texture_DifferenceVariance_DNA_5_03_256,0.00040575369540494035 +Cytoplasm_AreaShape_Zernike_3_3,0.0004089333757361581 +Image_Texture_DifferenceVariance_DNA_5_01_256,0.0004096954553795451 +Nuclei_Texture_DifferenceEntropy_ER_3_02_256,0.0004104955418294809 +Cytoplasm_RadialDistribution_FracAtD_RNA_1of4,0.000412499534092029 +Image_Texture_DifferenceVariance_Mito_3_01_256,0.0004128871162897268 +Image_Texture_DifferenceVariance_ER_5_01_256,0.00041618126497380413 +Image_Texture_DifferenceVariance_Mito_5_02_256,0.00042394412730665707 Nuclei_Correlation_Manders_RNA_AGP,0.0004243357297234672 -Nuclei_Correlation_Manders_RNA_DNA,-0.004786075390322817 -Nuclei_Correlation_Manders_RNA_ER,-0.00001613229172694085 -Nuclei_Correlation_Manders_RNA_Mito,-0.00026151671428208383 -Nuclei_Correlation_Overlap_AGP_DNA,-0.004017118054577785 -Nuclei_Correlation_Overlap_AGP_ER,0.0002359578101966504 -Nuclei_Correlation_Overlap_AGP_Mito,-0.0029134357307473804 +Image_Texture_DifferenceVariance_Mito_10_00_256,0.0004243516820475197 +Image_Texture_DifferenceVariance_ER_5_00_256,0.0004249594860324396 +Image_Texture_DifferenceVariance_Mito_5_01_256,0.0004252860005357676 +Image_Texture_DifferenceVariance_Mito_3_03_256,0.0004256622628286315 +Image_Texture_DifferenceVariance_Mito_10_02_256,0.00042648808697146566 +Image_Texture_DifferenceVariance_Mito_5_00_256,0.00042668941695217467 +Image_Texture_DifferenceVariance_ER_3_03_256,0.00043148710232604535 +Nuclei_Intensity_LowerQuartileIntensity_ER,0.0004317842214863355 +Image_Texture_DifferenceVariance_Mito_5_03_256,0.0004379849981364333 +Cells_Texture_AngularSecondMoment_AGP_5_03_256,0.0004406619777029908 +Cytoplasm_AreaShape_Zernike_8_2,0.000441794416449084 +Image_Texture_DifferenceVariance_ER_3_00_256,0.0004491521212058988 +Image_Texture_DifferenceVariance_ER_5_02_256,0.00045143117967601644 +Image_Intensity_UpperQuartileIntensity_RNA_BackgroundOnly,0.0004527679691911586 +Image_Texture_DifferenceVariance_ER_3_01_256,0.00045369728104351103 +Cells_Texture_AngularSecondMoment_AGP_10_02_256,0.0004686311026598673 +Nuclei_Correlation_Manders_Mito_AGP,0.00046997274308051154 +Image_Texture_DifferenceVariance_DNA_10_02_256,0.0004736311291795904 +Image_Texture_DifferenceVariance_ER_3_02_256,0.00047687466958640365 +Cytoplasm_RadialDistribution_RadialCV_DNA_1of4,0.000487161826373427 +Image_Texture_DifferenceVariance_DNA_10_00_256,0.0004901423610547228 +Nuclei_Correlation_Manders_ER_AGP,0.0004920489612936107 +Cells_AreaShape_Zernike_2_2,0.0004923100321046415 +Image_Texture_DifferenceVariance_DNA_10_03_256,0.0004951478794229272 +Cytoplasm_Texture_AngularSecondMoment_AGP_10_03_256,0.0004996439234971469 +Image_Intensity_StdIntensity_RNA_BackgroundOnly,0.0005009886446126466 +Image_Texture_Correlation_DNA_3_02_256,0.0005045523573507977 +Image_Texture_DifferenceVariance_DNA_10_01_256,0.0005047176939653468 +Image_Intensity_MedianIntensity_AGP__BackgroundOnly,0.0005127916742368626 +Image_Texture_AngularSecondMoment_ER_3_01_256,0.0005199587843538536 +Image_Intensity_MedianIntensity_Mito,0.0005249794558641311 +Image_Intensity_UpperQuartileIntensity_AGP__BackgroundOnly,0.0005376505612326385 +Cells_Intensity_MassDisplacement_RNA,0.0005424895629386195 +Image_ImageQuality_PercentMaximal_OrigMito,0.0005684682970652427 +Cells_Intensity_StdIntensityEdge_AGP,0.0005794793003774696 +Nuclei_AreaShape_Zernike_6_0,0.0005993417402824289 +Cells_Texture_AngularSecondMoment_AGP_5_01_256,0.0006037052875516912 +Cytoplasm_Texture_AngularSecondMoment_AGP_10_01_256,0.0006314514578533064 +Cytoplasm_Intensity_MeanIntensityEdge_ER,0.0006395645589933456 +Image_ImageQuality_ThresholdOtsu_OrigDNA_2W,0.0006428646874747663 +Image_Texture_InverseDifferenceMoment_DNA_3_01_256,0.0006496931246701542 +Cells_Intensity_StdIntensityEdge_RNA,0.0006575289564691067 +Cytoplasm_Correlation_RWC_Mito_ER,0.0006728117636032455 +Cytoplasm_RadialDistribution_FracAtD_ER_2of4,0.0006776350654647571 +Cells_Texture_AngularSecondMoment_AGP_5_00_256,0.0006915733994205921 +Nuclei_AreaShape_Solidity,0.0006917191170928796 +Cells_RadialDistribution_FracAtD_mito_tubeness_2of20,0.0006972421228549949 +Cells_Texture_AngularSecondMoment_AGP_3_03_256,0.0007295748701247511 +Image_Texture_DifferenceEntropy_AGP_10_03_256,0.0007423871938495308 +Cells_Texture_InfoMeas1_DNA_5_03_256,0.0007599190803156434 +Cells_Texture_DifferenceVariance_DNA_10_03_256,0.00076571431718479 +Cells_AreaShape_EulerNumber,0.000769243443498673 +Cytoplasm_AreaShape_Zernike_2_2,0.0007904087985744976 +Image_ImageQuality_MedianIntensity_OrigMito,0.0007922676259416893 +Cells_Correlation_RWC_RNA_ER,0.0007936692306437882 +Cells_Texture_DifferenceVariance_DNA_10_01_256,0.0007946481254649759 +Cytoplasm_AreaShape_Zernike_8_0,0.000805780537139424 +Cells_Texture_Entropy_RNA_10_00_256,0.0008106590194119911 +Nuclei_AreaShape_Zernike_6_4,0.0008202443485751942 +Nuclei_Texture_InfoMeas2_DNA_3_02_256,0.0008231085185132457 +Image_Intensity_UpperQuartileIntensity_Mito_BackgroundOnly,0.0008271786667290718 +Cells_Correlation_Manders_DNA_AGP,0.0008367885925382346 +Image_Texture_Correlation_DNA_3_01_256,0.000838929485534642 +Image_Intensity_MedianIntensity_Mito_BackgroundOnly,0.0008432296202621445 +Cells_Correlation_Manders_ER_AGP,0.0008549467714931774 +Cells_Texture_AngularSecondMoment_AGP_5_02_256,0.0008701224845282388 +Image_Intensity_LowerQuartileIntensity_Mito_BackgroundOnly,0.0008775513310729456 +Cells_Texture_DifferenceVariance_DNA_10_02_256,0.0008842745802944451 +Image_Intensity_MeanIntensity_Mito_BackgroundOnly,0.0008907937843832555 +Image_Intensity_LowerQuartileIntensity_Mito,0.0008975298494623031 +Cells_Texture_InfoMeas1_DNA_10_00_256,0.0009151161985209134 +Cytoplasm_Texture_AngularSecondMoment_AGP_10_00_256,0.000918973990191206 +Cells_Intensity_MaxIntensityEdge_AGP,0.0009255150944924547 +Image_ImageQuality_PercentMaximal_OrigRNA,0.0009392836607466644 +Cytoplasm_Intensity_UpperQuartileIntensity_AGP,0.0009447068256868544 +Cells_Texture_DifferenceVariance_DNA_10_00_256,0.000964226679272234 +Cells_Texture_DifferenceVariance_DNA_5_03_256,0.0009792672447121319 +Image_Texture_InfoMeas2_RNA_3_02_256,0.000988349570831105 +Cells_Texture_AngularSecondMoment_AGP_3_00_256,0.0009925398100662725 +Cytoplasm_AreaShape_Zernike_6_2,0.001002010439990778 +Cells_Texture_DifferenceVariance_DNA_5_01_256,0.0010029063130371483 +Cytoplasm_RadialDistribution_FracAtD_mito_tubeness_10of16,0.001010360764099231 Nuclei_Correlation_Overlap_AGP_RNA,0.0010146450995387442 -Nuclei_Correlation_Overlap_DNA_ER,-0.007712709547188858 -Nuclei_Correlation_Overlap_DNA_Mito,-0.023141807443896604 -Nuclei_Correlation_Overlap_DNA_RNA,-0.0036219783542594166 -Nuclei_Correlation_Overlap_ER_Mito,-0.005937432609275074 -Nuclei_Correlation_Overlap_ER_RNA,-0.0015295038501788735 -Nuclei_Correlation_Overlap_Mito_RNA,-0.011846634461191203 -Nuclei_Correlation_RWC_AGP_DNA,0.010534841741218255 -Nuclei_Correlation_RWC_AGP_ER,0.03055135312972377 -Nuclei_Correlation_RWC_AGP_Mito,0.021649645293912754 -Nuclei_Correlation_RWC_AGP_RNA,0.03306332157563262 -Nuclei_Correlation_RWC_DNA_AGP,0.018455153127876305 -Nuclei_Correlation_RWC_DNA_ER,0.0032535294467306297 -Nuclei_Correlation_RWC_DNA_Mito,-0.0018440070874052514 -Nuclei_Correlation_RWC_DNA_RNA,0.00025768550336341834 -Nuclei_Correlation_RWC_ER_AGP,0.031232336800977062 -Nuclei_Correlation_RWC_ER_DNA,-0.006099526863614511 -Nuclei_Correlation_RWC_ER_Mito,0.01448746177691924 -Nuclei_Correlation_RWC_ER_RNA,0.0022120710316312344 -Nuclei_Correlation_RWC_Mito_AGP,0.022246998949910647 -Nuclei_Correlation_RWC_Mito_DNA,-0.015418760606026337 -Nuclei_Correlation_RWC_Mito_ER,0.013446455819095024 -Nuclei_Correlation_RWC_Mito_RNA,0.004328391031640453 -Nuclei_Correlation_RWC_RNA_AGP,0.030740315563375153 -Nuclei_Correlation_RWC_RNA_DNA,-0.002917359605817422 +Image_Intensity_StdIntensity_AGP,0.0010188390860177935 +Cells_Texture_AngularSecondMoment_AGP_3_01_256,0.0010240051314721332 +Image_Texture_AngularSecondMoment_ER_3_02_256,0.0010248687584416133 +Cells_Texture_DifferenceVariance_DNA_5_02_256,0.0010421997088135974 +Cells_Texture_DifferenceVariance_DNA_3_03_256,0.001059095265889908 +Cells_Texture_DifferenceVariance_DNA_3_01_256,0.001063045647810249 +Image_Texture_DifferenceEntropy_DNA_3_03_256,0.0010885796077115609 +Cells_Texture_DifferenceVariance_DNA_5_00_256,0.0010897584537920217 +Cells_Texture_DifferenceVariance_DNA_3_02_256,0.0010937868669843244 +Cells_RadialDistribution_FracAtD_mito_tubeness_2of16,0.0011018922003767006 Nuclei_Correlation_RWC_RNA_ER,0.001124254049082841 -Nuclei_Correlation_RWC_RNA_Mito,0.004597828654280992 -Nuclei_Granularity_10_AGP,1.3566817940628313 -Nuclei_Granularity_10_DNA,-1.1239274600466198 -Nuclei_Granularity_10_ER,1.1416455968068933 -Nuclei_Granularity_10_Mito,1.0785010285659653 -Nuclei_Granularity_10_RNA,1.1299327244368662 -Nuclei_Granularity_11_AGP,0.7330938911958556 -Nuclei_Granularity_11_DNA,-0.3937484938258854 -Nuclei_Granularity_11_ER,0.5618080227186791 -Nuclei_Granularity_11_Mito,0.7444840561248358 -Nuclei_Granularity_11_RNA,0.8607832017845782 -Nuclei_Granularity_12_AGP,0.43449598190925187 -Nuclei_Granularity_12_DNA,-0.10748582732459294 -Nuclei_Granularity_12_ER,0.2326416838036235 -Nuclei_Granularity_12_Mito,0.21731003405919702 -Nuclei_Granularity_12_RNA,0.5156557846394603 -Nuclei_Granularity_13_AGP,0.20403389689884088 -Nuclei_Granularity_13_DNA,-0.014250316352503435 -Nuclei_Granularity_13_ER,-0.029923915492785502 -Nuclei_Granularity_13_Mito,-0.017868306645872634 -Nuclei_Granularity_13_RNA,0.3529651794169383 -Nuclei_Granularity_14_AGP,0.06425047227594576 -Nuclei_Granularity_14_DNA,0.008631436758740098 -Nuclei_Granularity_14_ER,-0.14801228233402955 -Nuclei_Granularity_14_Mito,-0.030916044068798693 -Nuclei_Granularity_14_RNA,0.14182821204906287 -Nuclei_Granularity_15_AGP,0.016073581428430152 -Nuclei_Granularity_15_DNA,0.013173614149330382 -Nuclei_Granularity_15_ER,-0.2502324667764101 -Nuclei_Granularity_15_Mito,-0.07750896987842326 -Nuclei_Granularity_15_RNA,-0.08578719191935283 -Nuclei_Granularity_16_AGP,0.060675663990203466 -Nuclei_Granularity_16_DNA,-0.005391498666579463 -Nuclei_Granularity_16_ER,-0.14625616845572742 -Nuclei_Granularity_16_Mito,-0.0799943126740501 -Nuclei_Granularity_16_RNA,-0.03957556620112379 -Nuclei_Granularity_1_AGP,-1.9717043084583024 -Nuclei_Granularity_1_DNA,-0.15247552115388485 -Nuclei_Granularity_1_ER,-1.1862287054804428 -Nuclei_Granularity_1_Mito,-4.642165748420295 -Nuclei_Granularity_1_RNA,-1.5934147376997094 -Nuclei_Granularity_2_AGP,-0.14903679840219353 -Nuclei_Granularity_2_DNA,0.11477119320452037 -Nuclei_Granularity_2_ER,-0.1717541736252757 -Nuclei_Granularity_2_Mito,0.18687569530999068 -Nuclei_Granularity_2_RNA,0.17919133581772975 -Nuclei_Granularity_3_AGP,0.06457974224170838 -Nuclei_Granularity_3_DNA,0.38880010977617785 -Nuclei_Granularity_3_ER,0.0813939078305136 -Nuclei_Granularity_3_Mito,0.33660854802395984 -Nuclei_Granularity_3_RNA,0.2586390563155606 -Nuclei_Granularity_4_AGP,0.12957924639482088 -Nuclei_Granularity_4_DNA,0.6458463626583402 -Nuclei_Granularity_4_ER,0.15011709430500164 -Nuclei_Granularity_4_Mito,0.5833093681546067 -Nuclei_Granularity_4_RNA,0.2419199589062303 -Nuclei_Granularity_5_AGP,0.21161160297370157 -Nuclei_Granularity_5_DNA,1.2857079613872453 -Nuclei_Granularity_5_ER,0.35541300842631546 -Nuclei_Granularity_5_Mito,0.6660515488684484 -Nuclei_Granularity_5_RNA,0.21821857093265024 -Nuclei_Granularity_6_AGP,0.5633331865150114 -Nuclei_Granularity_6_DNA,2.4961876300521038 -Nuclei_Granularity_6_ER,0.7379462298315729 -Nuclei_Granularity_6_Mito,0.7589750467586223 -Nuclei_Granularity_6_RNA,0.5441198858331013 -Nuclei_Granularity_7_AGP,1.0130981367701135 -Nuclei_Granularity_7_DNA,1.9587434010923332 -Nuclei_Granularity_7_ER,1.1588472225143622 -Nuclei_Granularity_7_Mito,1.2573406870196335 -Nuclei_Granularity_7_RNA,1.115735872005733 -Nuclei_Granularity_8_AGP,1.6342276131624622 -Nuclei_Granularity_8_DNA,-0.892053484114118 -Nuclei_Granularity_8_ER,1.595864485214514 -Nuclei_Granularity_8_Mito,1.536822222385533 -Nuclei_Granularity_8_RNA,1.488863483003238 -Nuclei_Granularity_9_AGP,1.8042275437419664 -Nuclei_Granularity_9_DNA,-1.742162690565185 -Nuclei_Granularity_9_ER,1.644756704623231 -Nuclei_Granularity_9_Mito,1.603964895579025 -Nuclei_Granularity_9_RNA,1.3509528270715996 -Nuclei_Intensity_IntegratedIntensityEdge_AGP,0.01643155932048493 -Nuclei_Intensity_IntegratedIntensityEdge_DNA,-0.3095657760197198 -Nuclei_Intensity_IntegratedIntensityEdge_ER,-0.1714371681503981 -Nuclei_Intensity_IntegratedIntensityEdge_Mito,0.7538700779579122 -Nuclei_Intensity_IntegratedIntensityEdge_RNA,-0.14317074011422126 -Nuclei_Intensity_IntegratedIntensity_AGP,-0.8690322812162323 -Nuclei_Intensity_IntegratedIntensity_DNA,-10.822329284719851 -Nuclei_Intensity_IntegratedIntensity_ER,-7.183810723862727 -Nuclei_Intensity_IntegratedIntensity_Mito,5.6031090218446975 -Nuclei_Intensity_IntegratedIntensity_RNA,-3.901261307648026 -Nuclei_Intensity_LowerQuartileIntensity_AGP,0.003829975806223321 -Nuclei_Intensity_LowerQuartileIntensity_DNA,-0.004609105762050718 -Nuclei_Intensity_LowerQuartileIntensity_ER,0.0004317842214863355 -Nuclei_Intensity_LowerQuartileIntensity_Mito,0.006676719432093959 -Nuclei_Intensity_LowerQuartileIntensity_RNA,0.004647760631107747 +Cytoplasm_Intensity_StdIntensity_AGP,0.0011284258063885395 +Cells_Texture_DifferenceVariance_DNA_3_00_256,0.0011367666566936747 +Cells_Texture_AngularSecondMoment_RNA_10_03_256,0.001146138394626179 +Cells_Intensity_MinIntensityEdge_Mito,0.0011585470151815865 +Cytoplasm_Intensity_MinIntensityEdge_Mito,0.0011586143109303239 +Image_Intensity_StdIntensity_ER,0.0011598957389503933 +Cytoplasm_Intensity_MinIntensity_Mito,0.0011604648436654686 +Cells_Intensity_MinIntensity_Mito,0.0011605091192904613 +Cells_Texture_AngularSecondMoment_RNA_10_01_256,0.0011616091767407966 +Cytoplasm_Correlation_Manders_ER_AGP,0.0011626391063526726 +Cytoplasm_Texture_AngularSecondMoment_AGP_10_02_256,0.0011627003149136377 +Cytoplasm_Texture_AngularSecondMoment_AGP_5_03_256,0.0011777631927639177 +Cytoplasm_AreaShape_Zernike_7_1,0.001185157510820069 +Image_Intensity_StdIntensity_RNA,0.0012179110833215692 +Cells_Texture_AngularSecondMoment_AGP_3_02_256,0.0012187218421034545 +Image_Texture_Correlation_RNA_10_01_256,0.0012581842362453816 +Cells_RadialDistribution_FracAtD_mito_tubeness_3of20,0.0012615778206119489 +Cytoplasm_Intensity_MeanIntensity_AGP,0.0012701998019556066 Nuclei_Intensity_MADIntensity_AGP,0.0013084672039674929 -Nuclei_Intensity_MADIntensity_DNA,0.000028147863058188352 -Nuclei_Intensity_MADIntensity_ER,0.0022577982002777875 -Nuclei_Intensity_MADIntensity_Mito,0.0022406395251918813 -Nuclei_Intensity_MADIntensity_RNA,0.0027670971016824065 -Nuclei_Intensity_MassDisplacement_AGP,0.1442126837892212 -Nuclei_Intensity_MassDisplacement_DNA,-0.01963150707187079 -Nuclei_Intensity_MassDisplacement_ER,0.174529926050897 -Nuclei_Intensity_MassDisplacement_Mito,0.32163427727991234 -Nuclei_Intensity_MassDisplacement_RNA,0.08315616491437808 -Nuclei_Intensity_MaxIntensityEdge_AGP,0.01083357744318426 -Nuclei_Intensity_MaxIntensityEdge_DNA,-0.0008918395023958352 -Nuclei_Intensity_MaxIntensityEdge_ER,0.015009199304737722 -Nuclei_Intensity_MaxIntensityEdge_Mito,0.02417174147723833 -Nuclei_Intensity_MaxIntensityEdge_RNA,0.009375841866183445 -Nuclei_Intensity_MaxIntensity_AGP,0.01054721154933966 -Nuclei_Intensity_MaxIntensity_DNA,-0.009288964623256957 -Nuclei_Intensity_MaxIntensity_ER,0.008775971471604974 -Nuclei_Intensity_MaxIntensity_Mito,0.024934383178826434 -Nuclei_Intensity_MaxIntensity_RNA,0.007875560494092392 -Nuclei_Intensity_MeanIntensityEdge_AGP,0.0033913524280365934 -Nuclei_Intensity_MeanIntensityEdge_DNA,-0.0011920534588324974 -Nuclei_Intensity_MeanIntensityEdge_ER,0.003281459820345004 -Nuclei_Intensity_MeanIntensityEdge_Mito,0.010298157095370337 -Nuclei_Intensity_MeanIntensityEdge_RNA,0.0038317268730653593 -Nuclei_Intensity_MeanIntensity_AGP,0.004625524241305636 -Nuclei_Intensity_MeanIntensity_DNA,-0.004358783015971621 -Nuclei_Intensity_MeanIntensity_ER,0.0030930922648843163 -Nuclei_Intensity_MeanIntensity_Mito,0.010074843575729372 -Nuclei_Intensity_MeanIntensity_RNA,0.006581165269556858 -Nuclei_Intensity_MedianIntensity_AGP,0.004438725972878647 -Nuclei_Intensity_MedianIntensity_DNA,-0.004102387787305671 -Nuclei_Intensity_MedianIntensity_ER,0.002711459080180105 -Nuclei_Intensity_MedianIntensity_Mito,0.008600584211989596 -Nuclei_Intensity_MedianIntensity_RNA,0.007047429338476954 -Nuclei_Intensity_MinIntensityEdge_AGP,0.0019461472811975336 -Nuclei_Intensity_MinIntensityEdge_DNA,-0.0008468257793361731 -Nuclei_Intensity_MinIntensityEdge_ER,-0.0012647794432243571 -Nuclei_Intensity_MinIntensityEdge_Mito,0.0035308628379129804 +Cells_Intensity_MeanIntensityEdge_Mito,0.0013098442931704824 +Cytoplasm_Texture_AngularSecondMoment_AGP_5_01_256,0.0013198675916958015 +Cells_Texture_Correlation_DNA_3_01_256,0.0013207004759784916 +Cytoplasm_Correlation_K_AGP_ER,0.0013297920498173688 +Cytoplasm_Texture_InverseDifferenceMoment_DNA_5_01_256,0.0013856446528572207 +Cytoplasm_Correlation_Manders_RNA_AGP,0.001400346853448166 +Cytoplasm_Correlation_Overlap_AGP_Mito,0.0014150914209323778 +Image_Texture_InfoMeas2_RNA_3_00_256,0.0014275075340943097 +Cytoplasm_Texture_AngularSecondMoment_AGP_5_00_256,0.0014513917609065815 +Cytoplasm_Texture_AngularSecondMoment_AGP_3_03_256,0.0014583244786306165 +Image_Texture_InfoMeas2_ER_3_00_256,0.0014645084275222522 +Cytoplasm_Intensity_LowerQuartileIntensity_Mito,0.0014677533200964867 +Cytoplasm_RadialDistribution_MeanFrac_AGP_4of4,0.0014808245100592092 +Cytoplasm_RadialDistribution_FracAtD_AGP_2of4,0.0014983329804199937 +Cells_Intensity_LowerQuartileIntensity_Mito,0.0015181955092362686 +Cytoplasm_AreaShape_Zernike_5_1,0.0015341685304077334 +Image_Texture_AngularSecondMoment_ER_3_00_256,0.0015455137212932873 +Cells_Intensity_MedianIntensity_Mito,0.001548076214734232 +Image_Texture_InfoMeas2_ER_3_02_256,0.0015826950622749468 +Cytoplasm_Texture_DifferenceVariance_DNA_10_03_256,0.0016044057689462272 +Cytoplasm_Texture_AngularSecondMoment_AGP_5_02_256,0.0016052483363797393 +Cytoplasm_RadialDistribution_FracAtD_DNA_2of4,0.0016273798939790947 +Cytoplasm_Intensity_MedianIntensity_Mito,0.0016440346669668697 +Cytoplasm_Texture_Entropy_AGP_5_03_256,0.001657128322423096 +Cells_Intensity_MaxIntensityEdge_RNA,0.0016614083994914682 +Cells_Texture_SumEntropy_RNA_3_00_256,0.001683898365181874 +Cytoplasm_Texture_DifferenceVariance_DNA_10_01_256,0.0017762985653080841 +Cells_Intensity_StdIntensity_AGP,0.0018014693680225842 Nuclei_Intensity_MinIntensityEdge_RNA,0.0018127218640590255 -Nuclei_Intensity_MinIntensity_AGP,0.0020040932946536936 -Nuclei_Intensity_MinIntensity_DNA,-0.000857472528214501 -Nuclei_Intensity_MinIntensity_ER,-0.0012328099950026225 -Nuclei_Intensity_MinIntensity_Mito,0.0035647717203988044 +Cytoplasm_Texture_AngularSecondMoment_RNA_10_01_256,0.0018160727990394478 +Cytoplasm_Intensity_UpperQuartileIntensity_Mito,0.0018164595803681959 +Cells_Texture_AngularSecondMoment_RNA_10_02_256,0.001829929286603956 +Cells_Intensity_UpperQuartileIntensity_AGP,0.0018318487169772806 +Cytoplasm_Texture_DifferenceVariance_DNA_10_02_256,0.001833420519536287 +Cytoplasm_Texture_AngularSecondMoment_AGP_3_00_256,0.0018463087373543838 +Cells_Texture_Correlation_DNA_3_00_256,0.0018546779429373995 +Cytoplasm_Texture_AngularSecondMoment_AGP_3_01_256,0.0018561818054017411 +Cells_Correlation_Manders_RNA_AGP,0.0018978955610010192 +Cells_Texture_AngularSecondMoment_RNA_10_00_256,0.0019089891948112039 +Cytoplasm_Texture_InverseDifferenceMoment_DNA_5_03_256,0.0019196891294920165 +Cytoplasm_Texture_AngularSecondMoment_RNA_10_03_256,0.0019377278703644582 +Nuclei_Intensity_MinIntensityEdge_AGP,0.0019461472811975336 +Image_Texture_InverseDifferenceMoment_DNA_5_00_256,0.001959829319147904 +Cytoplasm_RadialDistribution_FracAtD_RNA_2of4,0.0019633220773082327 Nuclei_Intensity_MinIntensity_RNA,0.001973019228101917 -Nuclei_Intensity_StdIntensityEdge_AGP,0.002734078563132411 -Nuclei_Intensity_StdIntensityEdge_DNA,-0.00008135364626326951 -Nuclei_Intensity_StdIntensityEdge_ER,0.006072481413758287 -Nuclei_Intensity_StdIntensityEdge_Mito,0.0065897443820394155 -Nuclei_Intensity_StdIntensityEdge_RNA,0.003627356981830746 -Nuclei_Intensity_StdIntensity_AGP,0.0023046261564111294 -Nuclei_Intensity_StdIntensity_DNA,-0.001411640642827134 -Nuclei_Intensity_StdIntensity_ER,0.002975996426559542 -Nuclei_Intensity_StdIntensity_Mito,0.004684819887543985 -Nuclei_Intensity_StdIntensity_RNA,0.0024746750428598055 -Nuclei_Intensity_UpperQuartileIntensity_AGP,0.005551785416257258 -Nuclei_Intensity_UpperQuartileIntensity_DNA,-0.004475139198635153 -Nuclei_Intensity_UpperQuartileIntensity_ER,0.005461399141035973 -Nuclei_Intensity_UpperQuartileIntensity_Mito,0.01201752956909307 -Nuclei_Intensity_UpperQuartileIntensity_RNA,0.009039973480975455 -Nuclei_Location_CenterMassIntensity_X_AGP,2.0223942194009554 -Nuclei_Location_CenterMassIntensity_X_DNA,1.9567254641836902 -Nuclei_Location_CenterMassIntensity_X_ER,1.964855889201111 -Nuclei_Location_CenterMassIntensity_X_Mito,1.9988290833029225 -Nuclei_Location_CenterMassIntensity_X_RNA,1.9698590587023563 -Nuclei_Location_CenterMassIntensity_Y_AGP,1.45603387541134 -Nuclei_Location_CenterMassIntensity_Y_DNA,1.498969735833514 -Nuclei_Location_CenterMassIntensity_Y_ER,1.4345942692944045 -Nuclei_Location_CenterMassIntensity_Y_Mito,1.4528460858070495 -Nuclei_Location_CenterMassIntensity_Y_RNA,1.4658684250214402 -Nuclei_Location_CenterMassIntensity_Z_AGP,0.0 -Nuclei_Location_CenterMassIntensity_Z_DNA,0.0 -Nuclei_Location_CenterMassIntensity_Z_ER,0.0 -Nuclei_Location_CenterMassIntensity_Z_Mito,0.0 -Nuclei_Location_CenterMassIntensity_Z_RNA,0.0 -Nuclei_Location_Center_X,1.9458065510737175 -Nuclei_Location_Center_Y,1.5408128762891515 -Nuclei_Location_Center_Z,0.0 -Nuclei_Location_MaxIntensity_X_AGP,1.8972030722061213 -Nuclei_Location_MaxIntensity_X_DNA,1.9555629824240421 -Nuclei_Location_MaxIntensity_X_ER,1.990245021647727 -Nuclei_Location_MaxIntensity_X_Mito,1.9196900141080366 -Nuclei_Location_MaxIntensity_X_RNA,1.773404013525564 -Nuclei_Location_MaxIntensity_Y_AGP,1.505455180117523 -Nuclei_Location_MaxIntensity_Y_DNA,1.2855941297233593 -Nuclei_Location_MaxIntensity_Y_ER,1.2606076308110348 -Nuclei_Location_MaxIntensity_Y_Mito,1.0825387279332426 -Nuclei_Location_MaxIntensity_Y_RNA,1.502248688432722 -Nuclei_Location_MaxIntensity_Z_AGP,0.0 -Nuclei_Location_MaxIntensity_Z_DNA,0.0 -Nuclei_Location_MaxIntensity_Z_ER,0.0 -Nuclei_Location_MaxIntensity_Z_Mito,0.0 -Nuclei_Location_MaxIntensity_Z_RNA,0.0 -Nuclei_Neighbors_AngleBetweenNeighbors_1,-0.3310192428376869 -Nuclei_Neighbors_FirstClosestDistance_1,0.9098170546736092 -Nuclei_Neighbors_FirstClosestObjectNumber_1,-0.6705452486044208 -Nuclei_Neighbors_NumberOfNeighbors_1,-0.007876676618341827 -Nuclei_Neighbors_PercentTouching_1,0.09700612990982371 -Nuclei_Neighbors_SecondClosestDistance_1,1.480876822843475 -Nuclei_Neighbors_SecondClosestObjectNumber_1,-0.5011810047760323 -Nuclei_Number_Object_Number,-0.635021273719006 -Nuclei_ObjectSkeleton_NumberBranchEnds_mito_skel,-1.967278609619418 -Nuclei_ObjectSkeleton_NumberNonTrunkBranches_mito_skel,-3.70442236746487 -Nuclei_ObjectSkeleton_NumberTrunks_mito_skel,-0.797397897305308 -Nuclei_ObjectSkeleton_TotalObjectSkeletonLength_mito_skel,-46.32449024969072 -Nuclei_Parent_NucleiIncludingEdges,-1.8973369306322891 -Nuclei_RadialDistribution_FracAtD_AGP_1of4,0.0038292480905468326 -Nuclei_RadialDistribution_FracAtD_AGP_2of4,0.009911890744990713 -Nuclei_RadialDistribution_FracAtD_AGP_3of4,0.005837269441865018 -Nuclei_RadialDistribution_FracAtD_AGP_4of4,-0.01996211657197747 -Nuclei_RadialDistribution_FracAtD_DNA_1of4,0.006132947561712465 -Nuclei_RadialDistribution_FracAtD_DNA_2of4,0.015514929180338456 -Nuclei_RadialDistribution_FracAtD_DNA_3of4,0.003461324227555503 -Nuclei_RadialDistribution_FracAtD_DNA_4of4,-0.024332188238703194 -Nuclei_RadialDistribution_FracAtD_ER_1of4,0.0038331270809381065 -Nuclei_RadialDistribution_FracAtD_ER_2of4,0.008563045385671837 -Nuclei_RadialDistribution_FracAtD_ER_3of4,6.853301556507003e-6 -Nuclei_RadialDistribution_FracAtD_ER_4of4,-0.0122521681802897 -Nuclei_RadialDistribution_FracAtD_Mito_1of4,0.002678267853499584 -Nuclei_RadialDistribution_FracAtD_Mito_2of4,0.004574515946960067 -Nuclei_RadialDistribution_FracAtD_Mito_3of4,-0.0010319793481378043 -Nuclei_RadialDistribution_FracAtD_Mito_4of4,-0.006816921616654876 -Nuclei_RadialDistribution_FracAtD_RNA_1of4,0.004677820717445255 -Nuclei_RadialDistribution_FracAtD_RNA_2of4,0.011153942630139038 -Nuclei_RadialDistribution_FracAtD_RNA_3of4,0.00040409527568573903 -Nuclei_RadialDistribution_FracAtD_RNA_4of4,-0.016521296287336097 -Nuclei_RadialDistribution_MeanFrac_AGP_1of4,0.016526805296464206 -Nuclei_RadialDistribution_MeanFrac_AGP_2of4,0.019609713404409645 -Nuclei_RadialDistribution_MeanFrac_AGP_3of4,0.013022857769475286 -Nuclei_RadialDistribution_MeanFrac_AGP_4of4,-0.02066300760855811 -Nuclei_RadialDistribution_MeanFrac_DNA_1of4,0.05262788313040642 -Nuclei_RadialDistribution_MeanFrac_DNA_2of4,0.04004039073207584 -Nuclei_RadialDistribution_MeanFrac_DNA_3of4,0.0057941120085649766 -Nuclei_RadialDistribution_MeanFrac_DNA_4of4,-0.03718595668982539 -Nuclei_RadialDistribution_MeanFrac_ER_1of4,0.002829521733779816 -Nuclei_RadialDistribution_MeanFrac_ER_2of4,-0.0017500142783610593 -Nuclei_RadialDistribution_MeanFrac_ER_3of4,-0.004827675469863232 -Nuclei_RadialDistribution_MeanFrac_ER_4of4,-0.006796828580248262 -Nuclei_RadialDistribution_MeanFrac_Mito_1of4,-0.002892264307935096 -Nuclei_RadialDistribution_MeanFrac_Mito_2of4,-0.007840887273729228 -Nuclei_RadialDistribution_MeanFrac_Mito_3of4,-0.008167678084980175 -Nuclei_RadialDistribution_MeanFrac_Mito_4of4,0.007421285148291996 -Nuclei_RadialDistribution_MeanFrac_RNA_1of4,0.01221950147035787 -Nuclei_RadialDistribution_MeanFrac_RNA_2of4,0.009761230751319388 -Nuclei_RadialDistribution_MeanFrac_RNA_3of4,-0.003671756667545229 -Nuclei_RadialDistribution_MeanFrac_RNA_4of4,-0.008872154448352593 -Nuclei_RadialDistribution_RadialCV_AGP_1of4,0.002828573379903058 -Nuclei_RadialDistribution_RadialCV_AGP_2of4,0.009677715795956214 -Nuclei_RadialDistribution_RadialCV_AGP_3of4,0.021000488032933314 -Nuclei_RadialDistribution_RadialCV_AGP_4of4,0.03212144340531615 -Nuclei_RadialDistribution_RadialCV_DNA_1of4,0.002199855333318685 -Nuclei_RadialDistribution_RadialCV_DNA_2of4,0.0034137407854633994 -Nuclei_RadialDistribution_RadialCV_DNA_3of4,0.005827422127539748 Nuclei_RadialDistribution_RadialCV_DNA_4of4,0.0019763658081860674 -Nuclei_RadialDistribution_RadialCV_ER_1of4,-0.0027418830765084832 -Nuclei_RadialDistribution_RadialCV_ER_2of4,0.008382653833428517 -Nuclei_RadialDistribution_RadialCV_ER_3of4,0.020243736262258047 -Nuclei_RadialDistribution_RadialCV_ER_4of4,0.04337517611648053 -Nuclei_RadialDistribution_RadialCV_Mito_1of4,0.00482536734829449 -Nuclei_RadialDistribution_RadialCV_Mito_2of4,0.015835565963058712 -Nuclei_RadialDistribution_RadialCV_Mito_3of4,0.0368550124190737 -Nuclei_RadialDistribution_RadialCV_Mito_4of4,0.06533181008828468 +Nuclei_Intensity_MinIntensity_AGP,0.0020040932946536936 +Image_Texture_Correlation_DNA_3_00_256,0.002022089385834751 +Cytoplasm_Intensity_StdIntensity_RNA,0.0020296064916012647 +Cytoplasm_Texture_DifferenceVariance_DNA_5_03_256,0.0020406608621529347 +Cells_RadialDistribution_FracAtD_mito_tubeness_4of20,0.002047018363635014 +Cytoplasm_Texture_DifferenceVariance_DNA_10_00_256,0.002056335299847185 +Image_Texture_InfoMeas2_RNA_3_01_256,0.002060006955940591 +Cytoplasm_Texture_AngularSecondMoment_AGP_3_02_256,0.002081632452098787 +Cytoplasm_Texture_DifferenceEntropy_AGP_5_01_256,0.0020879655271427155 +Cytoplasm_Texture_DifferenceVariance_DNA_5_01_256,0.0021149555644411587 +Cytoplasm_Texture_InverseDifferenceMoment_AGP_3_02_256,0.0021167261372668257 +Cytoplasm_Intensity_StdIntensity_ER,0.002139626416374333 +Cytoplasm_Texture_InfoMeas2_DNA_3_00_256,0.002142137861346335 +Cytoplasm_Texture_DifferenceVariance_DNA_5_02_256,0.0021567038477254686 +Image_ImageQuality_Correlation_OrigRNA_20,0.002157290592790845 +Cells_Intensity_MeanIntensity_AGP,0.002163212314908001 +Cells_Correlation_Manders_DNA_Mito,0.002168230950378489 +Cytoplasm_Texture_DifferenceVariance_DNA_3_01_256,0.002181285373270795 +Cells_RadialDistribution_FracAtD_mito_tubeness_3of16,0.0021838279124394033 +Cytoplasm_Texture_DifferenceVariance_DNA_3_03_256,0.002193547701096841 +Nuclei_RadialDistribution_RadialCV_DNA_1of4,0.002199855333318685 +Nuclei_Correlation_RWC_ER_RNA,0.0022120710316312344 +Image_ImageQuality_StdIntensity_OrigER,0.002214897752782014 +Cytoplasm_Intensity_MeanIntensityEdge_RNA,0.002216649088710337 +Image_Threshold_WeightedVariance_NucleiIncludingEdges,0.002222168200722529 +Cytoplasm_Texture_DifferenceVariance_DNA_3_02_256,0.002227106233641523 +Nuclei_Intensity_MADIntensity_Mito,0.0022406395251918813 +Cytoplasm_Texture_DifferenceVariance_DNA_5_00_256,0.0022510478820402507 +Nuclei_Intensity_MADIntensity_ER,0.0022577982002777875 +Image_ImageQuality_StdIntensity_OrigAGP,0.002260103495829822 +Nuclei_Intensity_StdIntensity_AGP,0.0023046261564111294 +Image_Texture_InverseDifferenceMoment_DNA_5_02_256,0.002306148280329751 +Cytoplasm_Intensity_MeanIntensityEdge_AGP,0.0023145496332955488 +Cytoplasm_RadialDistribution_MeanFrac_DNA_3of4,0.0023150428179614917 +Cytoplasm_Texture_DifferenceVariance_DNA_3_00_256,0.0023155112397384853 +Image_ImageQuality_MaxIntensity_OrigAGP,0.0023202355036649174 +Cytoplasm_Intensity_StdIntensityEdge_AGP,0.002355461528566267 +Image_Texture_InfoMeas2_ER_5_02_256,0.0023696804196532985 +Image_Intensity_MeanIntensity_Mito,0.0023752641821938787 +Image_Texture_InfoMeas2_RNA_3_03_256,0.002375718396011183 +Cells_Texture_AngularSecondMoment_ER_10_03_256,0.0024091646543366804 +Cells_Texture_InfoMeas1_DNA_10_01_256,0.0024143098613407903 +Cells_AreaShape_Zernike_1_1,0.0024400997788221563 +Nuclei_Intensity_StdIntensity_RNA,0.0024746750428598055 +Image_Texture_InfoMeas2_ER_3_01_256,0.0024755013367764893 +Cells_Texture_InfoMeas2_ER_3_02_256,0.00247629173782683 Nuclei_RadialDistribution_RadialCV_RNA_1of4,0.002487605331758456 -Nuclei_RadialDistribution_RadialCV_RNA_2of4,0.011416226213276013 -Nuclei_RadialDistribution_RadialCV_RNA_3of4,0.0142201366378157 -Nuclei_RadialDistribution_RadialCV_RNA_4of4,0.025185399593213302 -Nuclei_Texture_AngularSecondMoment_AGP_10_00_256,-0.008140609587022984 -Nuclei_Texture_AngularSecondMoment_AGP_10_01_256,-0.007707237800016991 -Nuclei_Texture_AngularSecondMoment_AGP_10_02_256,-0.008034118293601249 -Nuclei_Texture_AngularSecondMoment_AGP_10_03_256,-0.007692582471099661 -Nuclei_Texture_AngularSecondMoment_AGP_3_00_256,-0.009227579748464582 -Nuclei_Texture_AngularSecondMoment_AGP_3_01_256,-0.008596395067528535 -Nuclei_Texture_AngularSecondMoment_AGP_3_02_256,-0.009184717681207842 -Nuclei_Texture_AngularSecondMoment_AGP_3_03_256,-0.008423409649325107 -Nuclei_Texture_AngularSecondMoment_AGP_5_00_256,-0.008354481273273394 -Nuclei_Texture_AngularSecondMoment_AGP_5_01_256,-0.008463138990233493 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+Cells_Texture_SumVariance_Mito_10_02_256,40.872269495116655 +Cells_Texture_SumVariance_Mito_10_00_256,41.41611516334145 +Cells_Texture_SumVariance_Mito_5_03_256,44.266235793170665 +Cells_Texture_SumVariance_RNA_10_01_256,44.89138741853277 +Cells_Texture_SumVariance_ER_10_03_256,45.047617281701605 +Cells_Texture_SumVariance_Mito_5_01_256,45.05925514372819 +Cells_Texture_SumVariance_Mito_5_02_256,46.75800250645012 +Cells_Texture_SumVariance_Mito_5_00_256,46.798927349186144 +Cells_Texture_SumVariance_Mito_3_03_256,47.145134876095696 +Cells_Texture_SumVariance_ER_10_01_256,47.243159465707095 +Cells_Texture_SumVariance_Mito_3_01_256,47.50836739424815 +Cells_Texture_SumVariance_Mito_3_00_256,47.669280025315075 +Cells_Texture_SumVariance_Mito_3_02_256,47.795726647745724 +Cells_Texture_SumVariance_RNA_10_03_256,47.827867923571816 +Cells_Texture_SumVariance_ER_10_02_256,55.205822040057825 +Cells_Texture_SumVariance_RNA_10_02_256,56.520425711642915 +Cells_Texture_SumVariance_ER_10_00_256,56.552956433895275 +Cells_Texture_SumVariance_RNA_10_00_256,59.45869558000514 +Cells_Texture_SumVariance_ER_5_03_256,59.79662041521385 +Cells_Texture_SumVariance_ER_5_01_256,62.773543723515814 +Cells_Texture_SumVariance_ER_5_02_256,63.15962568813143 +Cells_Texture_SumVariance_ER_3_03_256,63.451110107919575 +Cells_Texture_SumVariance_ER_3_02_256,63.62002586537581 +Cells_Texture_SumVariance_ER_3_00_256,63.67195648955614 +Cells_Texture_SumVariance_ER_5_00_256,64.0001791518128 +Cells_Texture_SumVariance_RNA_5_01_256,64.2929236711142 +Cells_Texture_SumVariance_ER_3_01_256,64.76204378121558 +Cells_Texture_SumVariance_RNA_5_03_256,65.79215065581518 +Cells_Texture_SumVariance_RNA_5_02_256,69.08448509223177 +Cells_Texture_SumVariance_RNA_5_00_256,70.00980326228672 +Cells_Texture_SumVariance_RNA_3_01_256,70.79946797671535 +Cells_Texture_SumVariance_RNA_3_03_256,71.3057256765608 +Cells_Texture_SumVariance_RNA_3_02_256,72.47115993440647 +Cells_Texture_SumVariance_RNA_3_00_256,73.13045574041519 +Image_Intensity_TotalIntensity_ER__BackgroundOnly,194.55931844326582 +Image_Intensity_TotalIntensity_DNA_BackgroundOnly,200.5282319665576 +Image_Intensity_TotalIntensity_RNA_BackgroundOnly,303.95198986982746 +Image_Intensity_TotalIntensity_AGP__BackgroundOnly,438.1914455715048 +Image_Intensity_TotalIntensity_Mito_BackgroundOnly,827.6896507732106 +Image_Intensity_TotalIntensity_Mito,2770.188546500996 +Image_ImageQuality_TotalIntensity_OrigMito,3974.265574352251 +Image_Intensity_TotalArea_AGP__BackgroundOnly,30379.776961549003 +Image_Intensity_TotalArea_DNA_BackgroundOnly,30379.776961549003 +Image_Intensity_TotalArea_ER__BackgroundOnly,30379.776961549003 +Image_Intensity_TotalArea_Mito_BackgroundOnly,30379.776961549003 +Image_Intensity_TotalArea_RNA_BackgroundOnly,30379.776961549003 diff --git a/workspace/analysis/distances/gen_cosine_crispr_db_torch.py b/workspace/analysis/distances/gen_cosine_crispr_db_torch.py index c6b3423..f49b603 100644 --- a/workspace/analysis/distances/gen_cosine_crispr_db_torch.py +++ b/workspace/analysis/distances/gen_cosine_crispr_db_torch.py @@ -1,4 +1,5 @@ #!/usr/bin/env jupyter + # --- # jupyter: # jupytext: From 56e289d233314e283a0754c46398faf68aed4989 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Al=C3=A1n=20F=2E=20Mu=C3=B1oz?= Date: Fri, 26 Jan 2024 19:28:43 -0500 Subject: [PATCH 30/38] fix(RAB40B): remove plotting tools --- workspace/analysis/RAB40B/compare_features.py | 24 +++++++++---------- 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/workspace/analysis/RAB40B/compare_features.py b/workspace/analysis/RAB40B/compare_features.py index b9867cd..f7fda6f 100644 --- a/workspace/analysis/RAB40B/compare_features.py +++ b/workspace/analysis/RAB40B/compare_features.py @@ -19,19 +19,19 @@ genes_of_interest = ("RAB40B", "RAB40C", "INSYN1", "PIK3R3") -df = pl.read_parquet( fpath ) +df = pl.read_parquet(fpath) sub_df = df.filter(pl.col("Metadata_Symbol").is_in(genes_of_interest)) -groups = dict((x,f"{'RAB40B/C' if x.startswith('RAB') else 'INS/PIK'}") for x in genes_of_interest) +groups = dict( + (x, f"{'RAB40B/C' if x.startswith('RAB') else 'INS/PIK'}") + for x in genes_of_interest +) -clusters = sub_df.with_columns(pl.col("Metadata_Symbol").replace(groups).alias("Metadata_Cluster")) +clusters = sub_df.with_columns( + pl.col("Metadata_Symbol").replace(groups).alias("Metadata_Cluster") +) -medians=clusters.group_by("Metadata_Cluster").median() - wk -diff = medians.select(pl.all().exclude(pl.Utf8).diff()) -to_plot = diff.filter(~pl.all_horizontal(pl.all().is_null())).melt().to_pandas() -import seaborn as sns -import matplotlib.pyplot as plt -sns.scatterplot(data=to_plot, x="variable",y="value") -plt.savefig("distribution.png",dpi=200) -plt.close() +medians = clusters.group_by("Metadata_Cluster").median() +diff = medians.select(pl.all().exclude("^Metadata.*$").diff()) +feature_diff = diff.filter(~pl.all_horizontal(pl.all().is_null())).melt() +feature_diff.sort(by="value").write_csv("feature_diffs.csv") From f60da9b1696b55a8ad707aec42bd55414a52a733 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Al=C3=A1n=20F=2E=20Mu=C3=B1oz?= Date: Mon, 29 Jan 2024 09:33:55 -0500 Subject: [PATCH 31/38] feat(RAB40B): Incorporate feature Mann-Whitney --- workspace/analysis/RAB40B/compare_features.py | 36 +++++++++++++++++++ 1 file changed, 36 insertions(+) diff --git a/workspace/analysis/RAB40B/compare_features.py b/workspace/analysis/RAB40B/compare_features.py index f7fda6f..d0633ae 100644 --- a/workspace/analysis/RAB40B/compare_features.py +++ b/workspace/analysis/RAB40B/compare_features.py @@ -11,7 +11,10 @@ from pathlib import Path +import pandas as pd # TODO stick to polars import polars as pl +import scipy.stats as stats +from statsmodels.stats.multitest import multipletests fpath = Path( "/dgx1nas1/storage/data/shared/morphmap_profiles/orf/full_profiles_cc_adj_mean_corr.parquet" @@ -35,3 +38,36 @@ diff = medians.select(pl.all().exclude("^Metadata.*$").diff()) feature_diff = diff.filter(~pl.all_horizontal(pl.all().is_null())).melt() feature_diff.sort(by="value").write_csv("feature_diffs.csv") + + +cluster_a = clusters.filter(pl.col("Metadata_Cluster") == "RAB40B/C").select( + pl.all().exclude("^Metadata.*$") +) +cluster_b = clusters.filter(pl.col("Metadata_Cluster") == "INS/PIK").select( + pl.all().exclude("^Metadata.*$") +) +results = [] +for feature in cluster_a.columns: + cluster_A = cluster_a.get_column(feature) + cluster_B = cluster_b.get_column(feature) + + # Use t-test or Mann-Whitney U test based on data distribution + t_stat, p_val = stats.ttest_ind(cluster_A, cluster_B, equal_var=False) + + results.append({"Feature": feature, "T-Statistic": t_stat, "P-Value": p_val}) + + +results_df = pd.DataFrame(results) + +# drop rows with NaN +results_df = results_df.dropna() + +# Apply multiple testing correction + +# corrected_pvals = multipletests(results_df["P-Value"], method="fdr_bh")[1] +corrected_pvals = multipletests(results_df["P-Value"], method="fdr_bh")[1] +results_df["Corrected P-Value"] = corrected_pvals + +# Filter significant features +significant_features = results_df[results_df["Corrected P-Value"] < 0.05] +significant_features.to_csv("significant_features.csv") From 0596d10f7693c4856ea7d20fd4f5e744069d1bd4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Al=C3=A1n=20F=2E=20Mu=C3=B1oz?= Date: Mon, 29 Jan 2024 09:35:13 -0500 Subject: [PATCH 32/38] new(RAB40B): add significant features --- .../analysis/RAB40B/significant_features.csv | 1041 +++++++++++++++++ 1 file changed, 1041 insertions(+) create mode 100644 workspace/analysis/RAB40B/significant_features.csv diff --git a/workspace/analysis/RAB40B/significant_features.csv b/workspace/analysis/RAB40B/significant_features.csv new file mode 100644 index 0000000..c30e8a6 --- /dev/null +++ b/workspace/analysis/RAB40B/significant_features.csv @@ -0,0 +1,1041 @@ +,Feature,T-Statistic,P-Value,Corrected P-Value +2,Cells_AreaShape_BoundingBoxArea,-5.976490335972608,1.297999132115986e-05,0.00018097380449237397 +9,Cells_AreaShape_Compactness,-4.101472644076182,0.0005332464487974972,0.004085914347928875 +10,Cells_AreaShape_Eccentricity,-6.357984813591496,1.9212793412939844e-06,3.925730948444851e-05 +11,Cells_AreaShape_EquivalentDiameter,-3.0773136463276884,0.006183049278798634,0.03165292038604073 +13,Cells_AreaShape_Extent,5.465771994152371,1.4820993967740558e-05,0.00020276838123981287 +14,Cells_AreaShape_FormFactor,4.108522608125933,0.00045428883121548323,0.0035737388055618016 +15,Cells_AreaShape_MajorAxisLength,-7.029972217432823,1.0245348063783026e-06,2.382169599066792e-05 +16,Cells_AreaShape_MaxFeretDiameter,-6.568243349991114,2.4188823031900023e-06,4.6984051321221445e-05 +18,Cells_AreaShape_MeanRadius,2.8809636926284123,0.010177477513948511,0.04746807694252665 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100644 index 0000000..60c4d08 --- /dev/null +++ b/jump_examples/pyproject.toml @@ -0,0 +1,27 @@ +[tool.poetry] +name = "jump-examples" +version = "0.0.0" +description = "Examples that show how to interact with JUMP data" +authors = ["Alan Munoz"] +readme = "README.md" +packages = [{include = "jump_examples", from= "src"}] + +[tool.poetry.dependencies] +python = "^3.9" +boto3 = ">=1.33.1" +matplotlib = "^3.8.2" +polars = "^0.19.19" +pyarrow = "^14.0.1" +s3fs = "^2023.12.1" +seaborn = "^0.13.2" + +[tool.poetry.group.dev.dependencies] +jupyter = "^1.0.0" +jupytext = "^1.15.0" +pytest = "^7.4.1" +ruff-lsp = "^0.0.48" +ruff = "<0.2.0" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/jump_examples/src/jump_examples/basic.py b/jump_examples/src/jump_examples/basic.py new file mode 100644 index 0000000..627ca2a --- /dev/null +++ b/jump_examples/src/jump_examples/basic.py @@ -0,0 +1,65 @@ +#!/usr/bin/env jupyter +# --- +# jupyter: +# jupytext: +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.15.2 +# kernelspec: +# display_name: Python 3 +# language: python +# name: python3 +# --- + + +# %% [markdown] Overview +# This is a tutorial on how to access +# We will use polars to fetch the data frame lazily, with the help of s3fs and pyarrow. +# We prefer lazy loading because the data can be too big to be handled in memory. +# %% Imports +import polars as pl +from pyarrow.dataset import dataset +from s3fs import S3FileSystem + +# %% [markdown] +# The shapes of the available datasets are> +# - crispr: +# - orf: +# - compounds: +# The aws paths of the dataframes are shown below: +# %% Paths +prefix = ( + "s3://cellpainting-gallery/cpg0016-jump-integrated/source_all/workspace/profiles" +) +filepaths = dict( + crispr=f"{prefix}/chandrasekaran_2024_0000000/crispr/wellpos_cellcount_mad_outlier_nan_featselect_harmony.parquet", + orf=f"{prefix}/chandrasekaran_2024_0000000/orf/wellpos_cellcount_mad_outlier_nan_featselect_harmony.parquet", + compound=f"{prefix}/arevalo_2023_e834481/compound/mad_int_featselect_harmony.parquet/", +) + + +# %% [markdown] +# We use a S3FileSystem to avoid the need of credentials. +# %% +def lazy_load(path: str) -> pl.DataFrame: + fs = S3FileSystem(anon=True) + myds = dataset(path, filesystem=fs) + df = pl.scan_pyarrow_dataset(myds) + return df + + +# %% [markdown] +# We will lazy load the data to visualise its columns +# %% +print("Width, or number of columns.") +for name, path in filepaths.items(): + data = lazy_load(path) + metadata_cols = [col for col in data.columns if col.startswith("Metadata")] + print(f"{name}: {data.width}, containing {len(metadata_cols)} metadata columns") + +# %% [markdown] +# Let us now focus on the crispr dataset +# %% +data = lazy_load(filepaths["crispr"]) From 8e037b01cd8cc2f8d349da6f1b779ec06d464e4e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Al=C3=A1n=20F=2E=20Mu=C3=B1oz?= Date: Tue, 19 Mar 2024 01:10:30 -0400 Subject: [PATCH 34/38] change(examples): Aesthetic improvements --- jump_examples/src/jump_examples/basic.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/jump_examples/src/jump_examples/basic.py b/jump_examples/src/jump_examples/basic.py index 627ca2a..d9ce865 100644 --- a/jump_examples/src/jump_examples/basic.py +++ b/jump_examples/src/jump_examples/basic.py @@ -12,9 +12,8 @@ # language: python # name: python3 # --- - - # %% [markdown] Overview +# # Basic JUMP data access # This is a tutorial on how to access # We will use polars to fetch the data frame lazily, with the help of s3fs and pyarrow. # We prefer lazy loading because the data can be too big to be handled in memory. @@ -24,10 +23,11 @@ from s3fs import S3FileSystem # %% [markdown] -# The shapes of the available datasets are> -# - crispr: -# - orf: -# - compounds: +# The shapes of the available datasets are: +# - crispr: Knock-out genetic perturbations. +# - orf: Overexpression genetic perturbations. +# - compounds: Chemical genetic perturbations. +# # The aws paths of the dataframes are shown below: # %% Paths prefix = ( From 6bb778d1ae5757ec4e3b012b0b20173c3482156f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Al=C3=A1n=20F=2E=20Mu=C3=B1oz?= Date: Tue, 19 Mar 2024 01:20:43 -0400 Subject: [PATCH 35/38] change(pills): rename examples->pills --- {jump_examples => pills}/README.md | 0 {jump_examples => pills}/poetry.lock | 0 {jump_examples => pills}/pyproject.toml | 0 {jump_examples/src/jump_examples => pills/src}/basic.py | 0 4 files changed, 0 insertions(+), 0 deletions(-) rename {jump_examples => pills}/README.md (100%) rename {jump_examples => pills}/poetry.lock (100%) rename {jump_examples => pills}/pyproject.toml (100%) rename {jump_examples/src/jump_examples => pills/src}/basic.py (100%) diff --git a/jump_examples/README.md b/pills/README.md similarity index 100% rename from jump_examples/README.md rename to pills/README.md diff --git a/jump_examples/poetry.lock b/pills/poetry.lock similarity index 100% rename from jump_examples/poetry.lock rename to pills/poetry.lock diff --git a/jump_examples/pyproject.toml b/pills/pyproject.toml similarity index 100% rename from jump_examples/pyproject.toml rename to pills/pyproject.toml diff --git a/jump_examples/src/jump_examples/basic.py b/pills/src/basic.py similarity index 100% rename from jump_examples/src/jump_examples/basic.py rename to pills/src/basic.py From b83babe498a75b1aea53e18cfa6cebc833cd88e5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Al=C3=A1n=20F=2E=20Mu=C3=B1oz?= Date: Tue, 19 Mar 2024 14:14:25 -0400 Subject: [PATCH 36/38] change(pills): rename subfolder to scripts --- pills/{src => scripts}/basic.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) rename pills/{src => scripts}/basic.py (97%) diff --git a/pills/src/basic.py b/pills/scripts/basic.py similarity index 97% rename from pills/src/basic.py rename to pills/scripts/basic.py index d9ce865..69ecbd7 100644 --- a/pills/src/basic.py +++ b/pills/scripts/basic.py @@ -6,13 +6,13 @@ # extension: .py # format_name: percent # format_version: '1.3' -# jupytext_version: 1.15.2 +# jupytext_version: 1.16.1 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- -# %% [markdown] Overview +# %% Overview [markdown] # # Basic JUMP data access # This is a tutorial on how to access # We will use polars to fetch the data frame lazily, with the help of s3fs and pyarrow. From d269ef62a2cc419545ee8b57b5fafd56494aff34 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Al=C3=A1n=20F=2E=20Mu=C3=B1oz?= Date: Tue, 19 Mar 2024 14:14:41 -0400 Subject: [PATCH 37/38] change(pills): config script/notebook folders --- pills/pyproject.toml | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/pills/pyproject.toml b/pills/pyproject.toml index 60c4d08..609480e 100644 --- a/pills/pyproject.toml +++ b/pills/pyproject.toml @@ -1,10 +1,9 @@ [tool.poetry] -name = "jump-examples" +name = "pills" version = "0.0.0" description = "Examples that show how to interact with JUMP data" authors = ["Alan Munoz"] readme = "README.md" -packages = [{include = "jump_examples", from= "src"}] [tool.poetry.dependencies] python = "^3.9" @@ -22,6 +21,6 @@ pytest = "^7.4.1" ruff-lsp = "^0.0.48" ruff = "<0.2.0" -[build-system] -requires = ["poetry-core"] -build-backend = "poetry.core.masonry.api" +[tool.jupytext.formats] +"notebooks/" = ".qmd" +"scripts/" = "py:percent" From 3572a76e7f19e683f3d0e24363c25e28ebe327bc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Al=C3=A1n=20F=2E=20Mu=C3=B1oz?= Date: Tue, 19 Mar 2024 14:15:23 -0400 Subject: [PATCH 38/38] clean(RAB40B): remove commented line --- workspace/analysis/RAB40B/compare_features.py | 1 - 1 file changed, 1 deletion(-) diff --git a/workspace/analysis/RAB40B/compare_features.py b/workspace/analysis/RAB40B/compare_features.py index d0633ae..77d45a7 100644 --- a/workspace/analysis/RAB40B/compare_features.py +++ b/workspace/analysis/RAB40B/compare_features.py @@ -64,7 +64,6 @@ # Apply multiple testing correction -# corrected_pvals = multipletests(results_df["P-Value"], method="fdr_bh")[1] corrected_pvals = multipletests(results_df["P-Value"], method="fdr_bh")[1] results_df["Corrected P-Value"] = corrected_pvals