From d8e77604457330ede6deadb9f0c385c2ab775ea5 Mon Sep 17 00:00:00 2001 From: axiomcura Date: Wed, 11 Sep 2024 10:21:41 -0600 Subject: [PATCH 01/33] Updated docs on handling non-CellProfiler features --- README.md | 35 +++++++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) diff --git a/README.md b/README.md index c7dd62f5..86a5770a 100644 --- a/README.md +++ b/README.md @@ -113,6 +113,40 @@ normalized_df = pycytominer.normalize( samples="Metadata_broad_sample == 'DMSO'" ) ``` +### Handling Non-CellProfiler Morphological Features in Pycytominer + +In some cases, raw morphological features may not be extracted from CellProfiler. While Pycytominer fully supports features extracted by [`CellProfiler`](https://cellprofiler.org/), errors may occur when using features from other tools. + +To resolve this, you can manually specify the morphological features using the `features` [parameter](https://pycytominer.readthedocs.io/en/latest/pycytominer.html#pycytominer.normalize.normalize). +This parameter is also available in other key steps, such as [`aggregate`](https://pycytominer.readthedocs.io/en/latest/pycytominer.html#pycytominer.aggregate.aggregate), [`feature_select`](https://pycytominer.readthedocs.io/en/latest/pycytominer.html#pycytominer.feature_select.feature_select), and [`consensus`](https://pycytominer.readthedocs.io/en/latest/pycytominer.html#pycytominer.feature_select.feature_select). + +Below is an example of loading data that is not from [`CellProfiler`](https://cellprofiler.org/), demonstrating how to handle non-CellProfiler features. + +```python +# Real world example using a different dataset +import pandas as pd +import pycytominer + +commit = "da8ae6a3bc103346095d61b4ee02f08fc85a5d98" +url = f"https://media.githubusercontent.com/media/broadinstitute/lincs-cell-painting/{commit}/profiles/2016_04_01_a549_48hr_batch1/SQ00014812/SQ00014812_augmented.csv.gz" + +# assuming this is dataset was not generated by CellProfiler +df = pd.read_csv(url) + +# split the metadata and morphology features columns +metadata_features = df.columns[0:26].tolist() +morphology_features = df.columns[26::].tolist() + +# use the 'features' parameter to declare what features pycytominer should focus +normalized_df = pycytominer.normalize( + profiles=df, + features=morphology_features, + method="standardize", + samples="Metadata_broad_sample == 'DMSO'" +) +``` +**Note:** We are actively working on enhancing `pycytominer` to support morphological features extracted from a variety of software tools! + ### Pipeline orchestration @@ -140,6 +174,7 @@ Therefore, we have included some custom tools in `pycytominer/cyto_utils` that p - [Frameworks](#frameworks) - [API](#api) - [Usage](#usage) + - [Handling Non-CellProfiler Morphological Features in Pycytominer](#handling-non-cellprofiler-morphological-features-in-pycytominer) - [Pipeline orchestration](#pipeline-orchestration) - [Other functionality](#other-functionality) - [CellProfiler CSV collation](#cellprofiler-csv-collation) From 5aebe2cfe3948c2e2c860fb33d23a769820ec174 Mon Sep 17 00:00:00 2001 From: axiomcura Date: Wed, 11 Sep 2024 10:24:20 -0600 Subject: [PATCH 02/33] formatting --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 86a5770a..7130069c 100644 --- a/README.md +++ b/README.md @@ -113,6 +113,7 @@ normalized_df = pycytominer.normalize( samples="Metadata_broad_sample == 'DMSO'" ) ``` + ### Handling Non-CellProfiler Morphological Features in Pycytominer In some cases, raw morphological features may not be extracted from CellProfiler. While Pycytominer fully supports features extracted by [`CellProfiler`](https://cellprofiler.org/), errors may occur when using features from other tools. @@ -145,8 +146,8 @@ normalized_df = pycytominer.normalize( samples="Metadata_broad_sample == 'DMSO'" ) ``` -**Note:** We are actively working on enhancing `pycytominer` to support morphological features extracted from a variety of software tools! +**Note:** We are actively working on enhancing `pycytominer` to support morphological features extracted from a variety of software tools! ### Pipeline orchestration From 2f7b97156d6ed2f5e9d084d818ca1144f0bbae94 Mon Sep 17 00:00:00 2001 From: axiomcura Date: Wed, 11 Sep 2024 10:34:29 -0600 Subject: [PATCH 03/33] update docs indicating CellProfiler data was used --- README.md | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 7130069c..e81bc110 100644 --- a/README.md +++ b/README.md @@ -97,6 +97,8 @@ Each processing function has unique arguments, see our [documentation](https://p The default way to use pycytominer is within python scripts, and using pycytominer is simple and fun. +The example below demonstrates how to perform normalization using Pycytominer with a dataset generated by [CellProfiler](https://cellprofiler.org/). + ```python # Real world example import pandas as pd @@ -116,7 +118,8 @@ normalized_df = pycytominer.normalize( ### Handling Non-CellProfiler Morphological Features in Pycytominer -In some cases, raw morphological features may not be extracted from CellProfiler. While Pycytominer fully supports features extracted by [`CellProfiler`](https://cellprofiler.org/), errors may occur when using features from other tools. +In some cases, raw morphological features may not be extracted from CellProfiler. +While Pycytominer fully supports features extracted by [`CellProfiler`](https://cellprofiler.org/), errors may occur when using features from other tools. To resolve this, you can manually specify the morphological features using the `features` [parameter](https://pycytominer.readthedocs.io/en/latest/pycytominer.html#pycytominer.normalize.normalize). This parameter is also available in other key steps, such as [`aggregate`](https://pycytominer.readthedocs.io/en/latest/pycytominer.html#pycytominer.aggregate.aggregate), [`feature_select`](https://pycytominer.readthedocs.io/en/latest/pycytominer.html#pycytominer.feature_select.feature_select), and [`consensus`](https://pycytominer.readthedocs.io/en/latest/pycytominer.html#pycytominer.feature_select.feature_select). From 9c65c81df3c3c2589fc8591811dbe8d4174c3bfe Mon Sep 17 00:00:00 2001 From: axiomcura Date: Wed, 11 Sep 2024 10:35:40 -0600 Subject: [PATCH 04/33] typos and formatting --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index e81bc110..4cd21dda 100644 --- a/README.md +++ b/README.md @@ -97,7 +97,7 @@ Each processing function has unique arguments, see our [documentation](https://p The default way to use pycytominer is within python scripts, and using pycytominer is simple and fun. -The example below demonstrates how to perform normalization using Pycytominer with a dataset generated by [CellProfiler](https://cellprofiler.org/). +The example below demonstrates how to perform normalization with a dataset generated by [CellProfiler](https://cellprofiler.org/). ```python # Real world example @@ -118,7 +118,7 @@ normalized_df = pycytominer.normalize( ### Handling Non-CellProfiler Morphological Features in Pycytominer -In some cases, raw morphological features may not be extracted from CellProfiler. +In some cases, raw morphological features may not be extracted from [CellProfiler](https://cellprofiler.org/). While Pycytominer fully supports features extracted by [`CellProfiler`](https://cellprofiler.org/), errors may occur when using features from other tools. To resolve this, you can manually specify the morphological features using the `features` [parameter](https://pycytominer.readthedocs.io/en/latest/pycytominer.html#pycytominer.normalize.normalize). From 81497c6b024b312c297fdbca6cfa685983357cf5 Mon Sep 17 00:00:00 2001 From: axiomcura Date: Wed, 11 Sep 2024 11:02:09 -0600 Subject: [PATCH 05/33] added data structure documentation --- README.md | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) diff --git a/README.md b/README.md index 4cd21dda..9887dd3d 100644 --- a/README.md +++ b/README.md @@ -66,6 +66,24 @@ Pycytominer is primarily built on top of [pandas](https://pandas.pydata.org/docs Pycytominer currently supports [parquet](https://parquet.apache.org/) and compressed text file (e.g. `.csv.gz`) i/o. +## Data structure + +Pycytominer processes all data using [Pandas](https://pandas.pydata.org/) DataFrames. + +Currently, Pycytominer fully supports data generated by [CellProfiler]('https://cellprofiler.org/'), adhering to its specific data structure and naming conventions. + +CellProfiler-generated image-based profiles typically consist of two main components: + +- **Metadata features:** This section contains information about the experiment, such as plate ID, well position, incubation time, perturbation type, and other relevant experimental details. + +- **Morphology features:** These are the quantified morphological features captured from microscopy images. Naming of these features is structured like this + +The feature naming scheme in Pycytominer follows a specific structure. + +- **Metadata features:** These feature names are prefixed with `Metadata_`, indicating that the data in these columns contain metadata information. + +- **Morphological features:** These follow CellProfiler’s naming conventions, where default compartments are labeled as "cells," "cytoplasm," and "nuclei." If users have different compartments in their dataset, they will need to manually specify those compartments using the `compartments` [parameter](https://pycytominer.readthedocs.io/en/stable/pycytominer.cyto_utils.html#pycytominer.cyto_utils.cells.SingleCells.compartments). + ## API Pycytominer has five major processing functions: @@ -176,6 +194,7 @@ Therefore, we have included some custom tools in `pycytominer/cyto_utils` that p - [Data processing for image-based profiling](#data-processing-for-image-based-profiling) - [Installation](#installation) - [Frameworks](#frameworks) + - [Data structure](#data-structure) - [API](#api) - [Usage](#usage) - [Handling Non-CellProfiler Morphological Features in Pycytominer](#handling-non-cellprofiler-morphological-features-in-pycytominer) From e88cfdde7d757d07c832c0d2eb6ae581703e395a Mon Sep 17 00:00:00 2001 From: Erik Serrano <31600622+axiomcura@users.noreply.github.com> Date: Sat, 21 Sep 2024 18:42:12 -0600 Subject: [PATCH 06/33] Update README.md Co-authored-by: Gregory Way --- README.md | 1 - 1 file changed, 1 deletion(-) diff --git a/README.md b/README.md index 9887dd3d..55feb79b 100644 --- a/README.md +++ b/README.md @@ -168,7 +168,6 @@ normalized_df = pycytominer.normalize( ) ``` -**Note:** We are actively working on enhancing `pycytominer` to support morphological features extracted from a variety of software tools! ### Pipeline orchestration From 66e2c16e5373fff656a36a0b50fb097efc86b892 Mon Sep 17 00:00:00 2001 From: Erik Serrano <31600622+axiomcura@users.noreply.github.com> Date: Sat, 21 Sep 2024 18:42:16 -0600 Subject: [PATCH 07/33] Update README.md Co-authored-by: Gregory Way --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 55feb79b..9a25ac4b 100644 --- a/README.md +++ b/README.md @@ -78,7 +78,7 @@ CellProfiler-generated image-based profiles typically consist of two main compon - **Morphology features:** These are the quantified morphological features captured from microscopy images. Naming of these features is structured like this -The feature naming scheme in Pycytominer follows a specific structure. +These features follow a specific structure. - **Metadata features:** These feature names are prefixed with `Metadata_`, indicating that the data in these columns contain metadata information. From 7a7ee8f8d50a6fed935f4ec6a8e85fe87e9687ec Mon Sep 17 00:00:00 2001 From: Erik Serrano <31600622+axiomcura@users.noreply.github.com> Date: Sat, 21 Sep 2024 18:42:27 -0600 Subject: [PATCH 08/33] Update README.md Co-authored-by: Gregory Way --- README.md | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 9a25ac4b..b1df20c5 100644 --- a/README.md +++ b/README.md @@ -136,8 +136,11 @@ normalized_df = pycytominer.normalize( ### Handling Non-CellProfiler Morphological Features in Pycytominer -In some cases, raw morphological features may not be extracted from [CellProfiler](https://cellprofiler.org/). -While Pycytominer fully supports features extracted by [`CellProfiler`](https://cellprofiler.org/), errors may occur when using features from other tools. +Pycytominer also supports processing of raw morphological features from image analysis tools beyond [CellProfiler](https://cellprofiler.org/). +These tools include In Carta, Harmony, and others. +Using Pycytominer with these tools requires minor modifications to function arguments, and we encourage these users to pay particularly close attention to individual function documentation. + +If you are using Pycytominer with these other tools, we'd love to hear from you so that we can learn how to best support broad and multiple use-cases. To resolve this, you can manually specify the morphological features using the `features` [parameter](https://pycytominer.readthedocs.io/en/latest/pycytominer.html#pycytominer.normalize.normalize). This parameter is also available in other key steps, such as [`aggregate`](https://pycytominer.readthedocs.io/en/latest/pycytominer.html#pycytominer.aggregate.aggregate), [`feature_select`](https://pycytominer.readthedocs.io/en/latest/pycytominer.html#pycytominer.feature_select.feature_select), and [`consensus`](https://pycytominer.readthedocs.io/en/latest/pycytominer.html#pycytominer.feature_select.feature_select). From 576ec9441af321fc86b990dd5e00173c57ecebed Mon Sep 17 00:00:00 2001 From: Erik Serrano <31600622+axiomcura@users.noreply.github.com> Date: Sat, 21 Sep 2024 18:42:32 -0600 Subject: [PATCH 09/33] Update README.md Co-authored-by: Gregory Way --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index b1df20c5..a87d3908 100644 --- a/README.md +++ b/README.md @@ -142,7 +142,7 @@ Using Pycytominer with these tools requires minor modifications to function argu If you are using Pycytominer with these other tools, we'd love to hear from you so that we can learn how to best support broad and multiple use-cases. -To resolve this, you can manually specify the morphological features using the `features` [parameter](https://pycytominer.readthedocs.io/en/latest/pycytominer.html#pycytominer.normalize.normalize). +For example, to resolve potential feature issues in the `normalize()` function, you can manually specify the morphological features using the `features` [parameter](https://pycytominer.readthedocs.io/en/latest/pycytominer.html#pycytominer.normalize.normalize). This parameter is also available in other key steps, such as [`aggregate`](https://pycytominer.readthedocs.io/en/latest/pycytominer.html#pycytominer.aggregate.aggregate), [`feature_select`](https://pycytominer.readthedocs.io/en/latest/pycytominer.html#pycytominer.feature_select.feature_select), and [`consensus`](https://pycytominer.readthedocs.io/en/latest/pycytominer.html#pycytominer.feature_select.feature_select). Below is an example of loading data that is not from [`CellProfiler`](https://cellprofiler.org/), demonstrating how to handle non-CellProfiler features. From 3ef0a2f93bab74f3fe3829bba1818e52c05cd867 Mon Sep 17 00:00:00 2001 From: Erik Serrano <31600622+axiomcura@users.noreply.github.com> Date: Sat, 21 Sep 2024 18:42:38 -0600 Subject: [PATCH 10/33] Update README.md Co-authored-by: Gregory Way --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index a87d3908..02bf8ee9 100644 --- a/README.md +++ b/README.md @@ -143,7 +143,7 @@ Using Pycytominer with these tools requires minor modifications to function argu If you are using Pycytominer with these other tools, we'd love to hear from you so that we can learn how to best support broad and multiple use-cases. For example, to resolve potential feature issues in the `normalize()` function, you can manually specify the morphological features using the `features` [parameter](https://pycytominer.readthedocs.io/en/latest/pycytominer.html#pycytominer.normalize.normalize). -This parameter is also available in other key steps, such as [`aggregate`](https://pycytominer.readthedocs.io/en/latest/pycytominer.html#pycytominer.aggregate.aggregate), [`feature_select`](https://pycytominer.readthedocs.io/en/latest/pycytominer.html#pycytominer.feature_select.feature_select), and [`consensus`](https://pycytominer.readthedocs.io/en/latest/pycytominer.html#pycytominer.feature_select.feature_select). +This parameter is also available in other key steps, such as [`aggregate`](https://pycytominer.readthedocs.io/en/latest/pycytominer.html#pycytominer.aggregate.aggregate) and [`feature_select`](https://pycytominer.readthedocs.io/en/latest/pycytominer.html#pycytominer.feature_select.feature_select). Below is an example of loading data that is not from [`CellProfiler`](https://cellprofiler.org/), demonstrating how to handle non-CellProfiler features. From b31d8d5d9d6821d46a3d0684b0cbc562cbf2b6e1 Mon Sep 17 00:00:00 2001 From: axiomcura Date: Sat, 21 Sep 2024 19:04:25 -0600 Subject: [PATCH 11/33] added reviewer comments --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 02bf8ee9..88921619 100644 --- a/README.md +++ b/README.md @@ -82,7 +82,7 @@ These features follow a specific structure. - **Metadata features:** These feature names are prefixed with `Metadata_`, indicating that the data in these columns contain metadata information. -- **Morphological features:** These follow CellProfiler’s naming conventions, where default compartments are labeled as "cells," "cytoplasm," and "nuclei." If users have different compartments in their dataset, they will need to manually specify those compartments using the `compartments` [parameter](https://pycytominer.readthedocs.io/en/stable/pycytominer.cyto_utils.html#pycytominer.cyto_utils.cells.SingleCells.compartments). +- **Morphological features:** These follow CellProfiler’s naming conventions, where default compartments are labeled as "cells," "cytoplasm," and "**nuclei**." If users have different compartments in their dataset, they will need to manually specify those compartments using the `compartments` [parameter](https://pycytominer.readthedocs.io/en/stable/pycytominer.cyto_utils.html#pycytominer.cyto_utils.cells.SingleCells.compartments). ## API From 17a4520b88aa03f99c2423a11fe0defd300174f2 Mon Sep 17 00:00:00 2001 From: axiomcura Date: Mon, 23 Sep 2024 14:35:47 -0600 Subject: [PATCH 12/33] formatting --- README.md | 1 - 1 file changed, 1 deletion(-) diff --git a/README.md b/README.md index 88921619..6c0e740d 100644 --- a/README.md +++ b/README.md @@ -171,7 +171,6 @@ normalized_df = pycytominer.normalize( ) ``` - ### Pipeline orchestration Pycytominer is a collection of different functions with no explicit link between steps. From 9d8f223a9d41924ad2dbd93d27897b34e480eec4 Mon Sep 17 00:00:00 2001 From: Gregory Way Date: Thu, 26 Sep 2024 05:39:37 -0600 Subject: [PATCH 13/33] Simplify, restructure, and clarify --- README.md | 57 +++++++++++-------------------------------------------- 1 file changed, 11 insertions(+), 46 deletions(-) diff --git a/README.md b/README.md index 6c0e740d..8229344d 100644 --- a/README.md +++ b/README.md @@ -66,23 +66,25 @@ Pycytominer is primarily built on top of [pandas](https://pandas.pydata.org/docs Pycytominer currently supports [parquet](https://parquet.apache.org/) and compressed text file (e.g. `.csv.gz`) i/o. -## Data structure +### CellProfiler support -Pycytominer processes all data using [Pandas](https://pandas.pydata.org/) DataFrames. - -Currently, Pycytominer fully supports data generated by [CellProfiler]('https://cellprofiler.org/'), adhering to its specific data structure and naming conventions. +Currently, Pycytominer fully supports data generated by [CellProfiler]('https://cellprofiler.org/'), adhering defaults to its specific data structure and naming conventions. CellProfiler-generated image-based profiles typically consist of two main components: -- **Metadata features:** This section contains information about the experiment, such as plate ID, well position, incubation time, perturbation type, and other relevant experimental details. +- **Metadata features:** This section contains information about the experiment, such as plate ID, well position, incubation time, perturbation type, and other relevant experimental details. These feature names are prefixed with `Metadata_`, indicating that the data in these columns contain metadata information. +- **Morphology features:** These are the quantified morphological features captured from microscopy images. Thse feature names are prefixed with the default compartments ("Cells_", "Cytoplasm_", and "Nuclei_"). Pycytominer supports non-default compartment names. -- **Morphology features:** These are the quantified morphological features captured from microscopy images. Naming of these features is structured like this +### Handling inputs from other image analysis tools (other than CellProfiler) -These features follow a specific structure. +Pycytominer also supports processing of raw morphological features from image analysis tools beyond [CellProfiler](https://cellprofiler.org/). +These tools include In Carta, Harmony, and others. +Using Pycytominer with these tools requires minor modifications to function arguments, and we encourage these users to pay particularly close attention to individual function documentation. -- **Metadata features:** These feature names are prefixed with `Metadata_`, indicating that the data in these columns contain metadata information. +For example, to resolve potential feature issues in the `normalize()` function, you must manually specify the morphological features using the `features` [parameter](https://pycytominer.readthedocs.io/en/latest/pycytominer.html#pycytominer.normalize.normalize). +This parameter is also available in other key steps, such as [`aggregate`](https://pycytominer.readthedocs.io/en/latest/pycytominer.html#pycytominer.aggregate.aggregate) and [`feature_select`](https://pycytominer.readthedocs.io/en/latest/pycytominer.html#pycytominer.feature_select.feature_select). -- **Morphological features:** These follow CellProfiler’s naming conventions, where default compartments are labeled as "cells," "cytoplasm," and "**nuclei**." If users have different compartments in their dataset, they will need to manually specify those compartments using the `compartments` [parameter](https://pycytominer.readthedocs.io/en/stable/pycytominer.cyto_utils.html#pycytominer.cyto_utils.cells.SingleCells.compartments). +If you are using Pycytominer with these other tools, we'd love to hear from you so that we can learn how to best support broad and multiple use-cases. ## API @@ -134,43 +136,6 @@ normalized_df = pycytominer.normalize( ) ``` -### Handling Non-CellProfiler Morphological Features in Pycytominer - -Pycytominer also supports processing of raw morphological features from image analysis tools beyond [CellProfiler](https://cellprofiler.org/). -These tools include In Carta, Harmony, and others. -Using Pycytominer with these tools requires minor modifications to function arguments, and we encourage these users to pay particularly close attention to individual function documentation. - -If you are using Pycytominer with these other tools, we'd love to hear from you so that we can learn how to best support broad and multiple use-cases. - -For example, to resolve potential feature issues in the `normalize()` function, you can manually specify the morphological features using the `features` [parameter](https://pycytominer.readthedocs.io/en/latest/pycytominer.html#pycytominer.normalize.normalize). -This parameter is also available in other key steps, such as [`aggregate`](https://pycytominer.readthedocs.io/en/latest/pycytominer.html#pycytominer.aggregate.aggregate) and [`feature_select`](https://pycytominer.readthedocs.io/en/latest/pycytominer.html#pycytominer.feature_select.feature_select). - -Below is an example of loading data that is not from [`CellProfiler`](https://cellprofiler.org/), demonstrating how to handle non-CellProfiler features. - -```python -# Real world example using a different dataset -import pandas as pd -import pycytominer - -commit = "da8ae6a3bc103346095d61b4ee02f08fc85a5d98" -url = f"https://media.githubusercontent.com/media/broadinstitute/lincs-cell-painting/{commit}/profiles/2016_04_01_a549_48hr_batch1/SQ00014812/SQ00014812_augmented.csv.gz" - -# assuming this is dataset was not generated by CellProfiler -df = pd.read_csv(url) - -# split the metadata and morphology features columns -metadata_features = df.columns[0:26].tolist() -morphology_features = df.columns[26::].tolist() - -# use the 'features' parameter to declare what features pycytominer should focus -normalized_df = pycytominer.normalize( - profiles=df, - features=morphology_features, - method="standardize", - samples="Metadata_broad_sample == 'DMSO'" -) -``` - ### Pipeline orchestration Pycytominer is a collection of different functions with no explicit link between steps. From 308fd0f4633d6ba3b393a040bb16da92b0e56184 Mon Sep 17 00:00:00 2001 From: Gregory Way Date: Thu, 26 Sep 2024 05:43:35 -0600 Subject: [PATCH 14/33] Move singlecells note to CellProfiler support section Also delete table of contents, which was in a strange place --- README.md | 20 +++----------------- 1 file changed, 3 insertions(+), 17 deletions(-) diff --git a/README.md b/README.md index 8229344d..60981248 100644 --- a/README.md +++ b/README.md @@ -75,6 +75,9 @@ CellProfiler-generated image-based profiles typically consist of two main compon - **Metadata features:** This section contains information about the experiment, such as plate ID, well position, incubation time, perturbation type, and other relevant experimental details. These feature names are prefixed with `Metadata_`, indicating that the data in these columns contain metadata information. - **Morphology features:** These are the quantified morphological features captured from microscopy images. Thse feature names are prefixed with the default compartments ("Cells_", "Cytoplasm_", and "Nuclei_"). Pycytominer supports non-default compartment names. +Note, [`pycytominer.cyto_utils.cells.SingleCells()`](pycytominer/cyto_utils/cells.py) contains code to interact with single-cell SQLite files, which are output from CellProfiler. +Processing capabilities for SQLite files depends on SQLite file size and your available computational resources (for ex. memory and cores). + ### Handling inputs from other image analysis tools (other than CellProfiler) Pycytominer also supports processing of raw morphological features from image analysis tools beyond [CellProfiler](https://cellprofiler.org/). @@ -157,23 +160,6 @@ And, more specifically than that, image-based profiling readouts from [CellProfi Therefore, we have included some custom tools in `pycytominer/cyto_utils` that provides other functionality: -- [Data processing for image-based profiling](#data-processing-for-image-based-profiling) - - [Installation](#installation) - - [Frameworks](#frameworks) - - [Data structure](#data-structure) - - [API](#api) - - [Usage](#usage) - - [Handling Non-CellProfiler Morphological Features in Pycytominer](#handling-non-cellprofiler-morphological-features-in-pycytominer) - - [Pipeline orchestration](#pipeline-orchestration) - - [Other functionality](#other-functionality) - - [CellProfiler CSV collation](#cellprofiler-csv-collation) - - [Creating a cell locations lookup table](#creating-a-cell-locations-lookup-table) - - [Generating a GCT file for morpheus](#generating-a-gct-file-for-morpheus) - - [Citing pycytominer](#citing-pycytominer) - -Note, [`pycytominer.cyto_utils.cells.SingleCells()`](pycytominer/cyto_utils/cells.py) contains code to interact with single-cell SQLite files, which are output from CellProfiler. -Processing capabilities for SQLite files depends on SQLite file size and your available computational resources (for ex. memory and cores). - ### CellProfiler CSV collation If running your images on a cluster, unless you have a MySQL or similar large database set up then you will likely end up with lots of different folders from the different cluster runs (often one per well or one per site), each one containing an `Image.csv`, `Nuclei.csv`, etc. From 936b2724c492ef0085b9f90bd7959e6f4aaa7afd Mon Sep 17 00:00:00 2001 From: Gregory Way Date: Thu, 26 Sep 2024 06:06:27 -0600 Subject: [PATCH 15/33] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 60981248..beb0af15 100644 --- a/README.md +++ b/README.md @@ -73,7 +73,7 @@ Currently, Pycytominer fully supports data generated by [CellProfiler]('https:// CellProfiler-generated image-based profiles typically consist of two main components: - **Metadata features:** This section contains information about the experiment, such as plate ID, well position, incubation time, perturbation type, and other relevant experimental details. These feature names are prefixed with `Metadata_`, indicating that the data in these columns contain metadata information. -- **Morphology features:** These are the quantified morphological features captured from microscopy images. Thse feature names are prefixed with the default compartments ("Cells_", "Cytoplasm_", and "Nuclei_"). Pycytominer supports non-default compartment names. +- **Morphology features:** These are the quantified morphological features prefixed with the default compartments ("Cells_", "Cytoplasm_", and "Nuclei_"). Pycytominer also supports non-default compartment names. Note, [`pycytominer.cyto_utils.cells.SingleCells()`](pycytominer/cyto_utils/cells.py) contains code to interact with single-cell SQLite files, which are output from CellProfiler. Processing capabilities for SQLite files depends on SQLite file size and your available computational resources (for ex. memory and cores). From 4cb70ecd7b502814b14778266475fe1c66030ac5 Mon Sep 17 00:00:00 2001 From: Gregory Way Date: Thu, 26 Sep 2024 06:17:30 -0600 Subject: [PATCH 16/33] to make precommit prettier hook happy --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index beb0af15..8da96107 100644 --- a/README.md +++ b/README.md @@ -73,6 +73,7 @@ Currently, Pycytominer fully supports data generated by [CellProfiler]('https:// CellProfiler-generated image-based profiles typically consist of two main components: - **Metadata features:** This section contains information about the experiment, such as plate ID, well position, incubation time, perturbation type, and other relevant experimental details. These feature names are prefixed with `Metadata_`, indicating that the data in these columns contain metadata information. + - **Morphology features:** These are the quantified morphological features prefixed with the default compartments ("Cells_", "Cytoplasm_", and "Nuclei_"). Pycytominer also supports non-default compartment names. Note, [`pycytominer.cyto_utils.cells.SingleCells()`](pycytominer/cyto_utils/cells.py) contains code to interact with single-cell SQLite files, which are output from CellProfiler. From 073de23459204388c54e127f667d3684c2206c8a Mon Sep 17 00:00:00 2001 From: Gregory Way Date: Thu, 26 Sep 2024 06:20:31 -0600 Subject: [PATCH 17/33] Prettier hook catching something it should not --- README.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/README.md b/README.md index 8da96107..61d9934b 100644 --- a/README.md +++ b/README.md @@ -73,8 +73,7 @@ Currently, Pycytominer fully supports data generated by [CellProfiler]('https:// CellProfiler-generated image-based profiles typically consist of two main components: - **Metadata features:** This section contains information about the experiment, such as plate ID, well position, incubation time, perturbation type, and other relevant experimental details. These feature names are prefixed with `Metadata_`, indicating that the data in these columns contain metadata information. - -- **Morphology features:** These are the quantified morphological features prefixed with the default compartments ("Cells_", "Cytoplasm_", and "Nuclei_"). Pycytominer also supports non-default compartment names. +- **Morphology features:** These are the quantified morphological features prefixed with the default compartments (`Cells_`, `Cytoplasm_`, and `Nuclei_`). Pycytominer also supports non-default compartment names. Note, [`pycytominer.cyto_utils.cells.SingleCells()`](pycytominer/cyto_utils/cells.py) contains code to interact with single-cell SQLite files, which are output from CellProfiler. Processing capabilities for SQLite files depends on SQLite file size and your available computational resources (for ex. memory and cores). From 6ba350269d117d5055b19b7627eb951c22219348 Mon Sep 17 00:00:00 2001 From: Erik Serrano <31600622+axiomcura@users.noreply.github.com> Date: Thu, 26 Sep 2024 12:55:46 -0600 Subject: [PATCH 18/33] Update README.md Co-authored-by: Dave Bunten --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 61d9934b..3150df7a 100644 --- a/README.md +++ b/README.md @@ -81,7 +81,7 @@ Processing capabilities for SQLite files depends on SQLite file size and your av ### Handling inputs from other image analysis tools (other than CellProfiler) Pycytominer also supports processing of raw morphological features from image analysis tools beyond [CellProfiler](https://cellprofiler.org/). -These tools include In Carta, Harmony, and others. +These tools include [In Carta](https://www.moleculardevices.com/products/cellular-imaging-systems/high-content-analysis/in-carta-image-analysis-software), [Harmony](https://www.revvity.com/product/harmony-5-2-office-revvity-hh17000019#product-overview), and others. Using Pycytominer with these tools requires minor modifications to function arguments, and we encourage these users to pay particularly close attention to individual function documentation. For example, to resolve potential feature issues in the `normalize()` function, you must manually specify the morphological features using the `features` [parameter](https://pycytominer.readthedocs.io/en/latest/pycytominer.html#pycytominer.normalize.normalize). From bc5de22da6d1db6dfb35d9734e5bfe00966114b1 Mon Sep 17 00:00:00 2001 From: Erik Serrano <31600622+axiomcura@users.noreply.github.com> Date: Thu, 26 Sep 2024 12:55:59 -0600 Subject: [PATCH 19/33] Update README.md Co-authored-by: Dave Bunten --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 3150df7a..e55d4fe8 100644 --- a/README.md +++ b/README.md @@ -68,7 +68,7 @@ Pycytominer currently supports [parquet](https://parquet.apache.org/) and compre ### CellProfiler support -Currently, Pycytominer fully supports data generated by [CellProfiler]('https://cellprofiler.org/'), adhering defaults to its specific data structure and naming conventions. +Currently, Pycytominer fully supports data generated by [CellProfiler](https://cellprofiler.org/), adhering defaults to its specific data structure and naming conventions. CellProfiler-generated image-based profiles typically consist of two main components: From 7b22711205d56f7b3ea88da0f1696ac5eba36ef4 Mon Sep 17 00:00:00 2001 From: Erik Serrano <31600622+axiomcura@users.noreply.github.com> Date: Thu, 26 Sep 2024 13:17:58 -0600 Subject: [PATCH 20/33] Update README.md Co-authored-by: Dave Bunten --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index e55d4fe8..eff4902f 100644 --- a/README.md +++ b/README.md @@ -75,7 +75,7 @@ CellProfiler-generated image-based profiles typically consist of two main compon - **Metadata features:** This section contains information about the experiment, such as plate ID, well position, incubation time, perturbation type, and other relevant experimental details. These feature names are prefixed with `Metadata_`, indicating that the data in these columns contain metadata information. - **Morphology features:** These are the quantified morphological features prefixed with the default compartments (`Cells_`, `Cytoplasm_`, and `Nuclei_`). Pycytominer also supports non-default compartment names. -Note, [`pycytominer.cyto_utils.cells.SingleCells()`](pycytominer/cyto_utils/cells.py) contains code to interact with single-cell SQLite files, which are output from CellProfiler. +Note, [`pycytominer.cyto_utils.cells.SingleCells()`](pycytominer/cyto_utils/cells.py) contains code designed to interact with single-cell SQLite files exported from CellProfiler. Processing capabilities for SQLite files depends on SQLite file size and your available computational resources (for ex. memory and cores). ### Handling inputs from other image analysis tools (other than CellProfiler) From 0c3d622f63cb84a14e26146cbbe5f7fad2a4028f Mon Sep 17 00:00:00 2001 From: Erik Serrano <31600622+axiomcura@users.noreply.github.com> Date: Thu, 26 Sep 2024 13:18:05 -0600 Subject: [PATCH 21/33] Update README.md Co-authored-by: Dave Bunten --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index eff4902f..5d311e7b 100644 --- a/README.md +++ b/README.md @@ -76,7 +76,7 @@ CellProfiler-generated image-based profiles typically consist of two main compon - **Morphology features:** These are the quantified morphological features prefixed with the default compartments (`Cells_`, `Cytoplasm_`, and `Nuclei_`). Pycytominer also supports non-default compartment names. Note, [`pycytominer.cyto_utils.cells.SingleCells()`](pycytominer/cyto_utils/cells.py) contains code designed to interact with single-cell SQLite files exported from CellProfiler. -Processing capabilities for SQLite files depends on SQLite file size and your available computational resources (for ex. memory and cores). +Processing capabilities for SQLite files depends on SQLite file size and your available computational resources (for ex. memory and CPU). ### Handling inputs from other image analysis tools (other than CellProfiler) From dc8467ba3417de4073842f61c29e16beaa14bf8c Mon Sep 17 00:00:00 2001 From: Erik Serrano <31600622+axiomcura@users.noreply.github.com> Date: Thu, 26 Sep 2024 13:18:13 -0600 Subject: [PATCH 22/33] Update README.md Co-authored-by: Dave Bunten --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 5d311e7b..75cf0e96 100644 --- a/README.md +++ b/README.md @@ -85,7 +85,7 @@ These tools include [In Carta](https://www.moleculardevices.com/products/cellula Using Pycytominer with these tools requires minor modifications to function arguments, and we encourage these users to pay particularly close attention to individual function documentation. For example, to resolve potential feature issues in the `normalize()` function, you must manually specify the morphological features using the `features` [parameter](https://pycytominer.readthedocs.io/en/latest/pycytominer.html#pycytominer.normalize.normalize). -This parameter is also available in other key steps, such as [`aggregate`](https://pycytominer.readthedocs.io/en/latest/pycytominer.html#pycytominer.aggregate.aggregate) and [`feature_select`](https://pycytominer.readthedocs.io/en/latest/pycytominer.html#pycytominer.feature_select.feature_select). +The `features` parameter is also available in other key steps, such as [`aggregate`](https://pycytominer.readthedocs.io/en/latest/pycytominer.html#pycytominer.aggregate.aggregate) and [`feature_select`](https://pycytominer.readthedocs.io/en/latest/pycytominer.html#pycytominer.feature_select.feature_select). If you are using Pycytominer with these other tools, we'd love to hear from you so that we can learn how to best support broad and multiple use-cases. From 494edf4ae173ceba22a98683b682ea94383871f3 Mon Sep 17 00:00:00 2001 From: Gregory Way Date: Thu, 26 Sep 2024 13:27:53 -0600 Subject: [PATCH 23/33] Minor fixes to README after review --- README.md | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 75cf0e96..8c832f0d 100644 --- a/README.md +++ b/README.md @@ -73,7 +73,7 @@ Currently, Pycytominer fully supports data generated by [CellProfiler](https://c CellProfiler-generated image-based profiles typically consist of two main components: - **Metadata features:** This section contains information about the experiment, such as plate ID, well position, incubation time, perturbation type, and other relevant experimental details. These feature names are prefixed with `Metadata_`, indicating that the data in these columns contain metadata information. -- **Morphology features:** These are the quantified morphological features prefixed with the default compartments (`Cells_`, `Cytoplasm_`, and `Nuclei_`). Pycytominer also supports non-default compartment names. +- **Morphology features:** These are the quantified morphological features prefixed with the default compartments (`Cells_`, `Cytoplasm_`, and `Nuclei_`). Pycytominer also supports non-default compartment names (e.g., `Mito_`). Note, [`pycytominer.cyto_utils.cells.SingleCells()`](pycytominer/cyto_utils/cells.py) contains code designed to interact with single-cell SQLite files exported from CellProfiler. Processing capabilities for SQLite files depends on SQLite file size and your available computational resources (for ex. memory and CPU). @@ -87,7 +87,8 @@ Using Pycytominer with these tools requires minor modifications to function argu For example, to resolve potential feature issues in the `normalize()` function, you must manually specify the morphological features using the `features` [parameter](https://pycytominer.readthedocs.io/en/latest/pycytominer.html#pycytominer.normalize.normalize). The `features` parameter is also available in other key steps, such as [`aggregate`](https://pycytominer.readthedocs.io/en/latest/pycytominer.html#pycytominer.aggregate.aggregate) and [`feature_select`](https://pycytominer.readthedocs.io/en/latest/pycytominer.html#pycytominer.feature_select.feature_select). -If you are using Pycytominer with these other tools, we'd love to hear from you so that we can learn how to best support broad and multiple use-cases. +If you are using Pycytominer with these other tools, please file [an issue](https://github.com/cytomining/pycytominer/issues) to reach out. +We'd love to hear from you so that we can learn how to best support broad and multiple use-cases. ## API @@ -238,7 +239,7 @@ pycytominer.cyto_utils.write_gct( ) ``` -## Citing pycytominer +## Citing Pycytominer If you have used `pycytominer` in your project, please use the citation below. You can also find the citation in the 'cite this repository' link at the top right under `about` section. From da83c2881d4861886170bf59294840fc56f63901 Mon Sep 17 00:00:00 2001 From: axiomcura Date: Thu, 26 Sep 2024 13:44:35 -0600 Subject: [PATCH 24/33] added new figure for the pycytominer pipeline, renamed old pipeline as legacy --- README.md | 2 +- media/legacy_pipeline.png | Bin 0 -> 158162 bytes media/pipeline.png | Bin 158162 -> 726255 bytes 3 files changed, 1 insertion(+), 1 deletion(-) create mode 100644 media/legacy_pipeline.png diff --git a/README.md b/README.md index 8c832f0d..133ccf41 100644 --- a/README.md +++ b/README.md @@ -11,7 +11,7 @@ Pycytominer is a suite of common functions used to process high dimensional readouts from high-throughput cell experiments. The tool is most often used for processing data through the following pipeline: -Description of the pycytominer pipeline. Images flow from feature extraction and are processed with a series of steps +Description of the pycytominer pipeline. 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The tool is most often used for processing data through the following pipeline: -Description of the pycytominer pipeline. Images flow from feature extraction and are processed with a series of steps +Description of the pycytominer pipeline. Images flow from feature extraction and are processed with a series of steps -[Click here for high resolution pipeline image](https://github.com/cytomining/pycytominer/blob/main/media/pipeline.png) +[Click here for high resolution pipeline image]("./media/pipeline.png) Image data flow from a microscope to cell segmentation and feature extraction tools (e.g. CellProfiler or DeepProfiler). From here, additional single cell processing tools curate the single cell readouts into a form manageable for pycytominer input. From 956c9df6188ad56eac7be4ae384d27152b6ff82c Mon Sep 17 00:00:00 2001 From: axiomcura Date: Thu, 26 Sep 2024 13:52:47 -0600 Subject: [PATCH 26/33] added figure caption --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index a20c04c1..132f4917 100644 --- a/README.md +++ b/README.md @@ -13,6 +13,8 @@ The tool is most often used for processing data through the following pipeline: Description of the pycytominer pipeline. Images flow from feature extraction and are processed with a series of steps +> Figure 1. The standard image-based profiling experiment and the role of Pycytominer. (A) In the experimental phase, a scientist plates cells, often perturbing them with chemical or genetic agents and performs microscopy imaging. In image analysis, using CellProfiler for example, a scientist applies several data processing steps to generate image-based profiles. In addition, scientists can apply a more flexible approach by using deep learning models, such as DeepProfiler, to generate image-based profiles. (B) Pycytominer performs image-based profiling to process morphology features and make them ready for downstream analyses. (C) Pycytominer performs five fundamental functions, each implemented with a simple and intuitive API. Each function enables a user to implement various methods for executing operations. + [Click here for high resolution pipeline image]("./media/pipeline.png) Image data flow from a microscope to cell segmentation and feature extraction tools (e.g. CellProfiler or DeepProfiler). From 61f52d0f5371b27ba4cbbebd470d6642b3392e92 Mon Sep 17 00:00:00 2001 From: axiomcura Date: Thu, 26 Sep 2024 13:56:06 -0600 Subject: [PATCH 27/33] reduced main figure height --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 132f4917..44553e47 100644 --- a/README.md +++ b/README.md @@ -11,7 +11,7 @@ Pycytominer is a suite of common functions used to process high dimensional readouts from high-throughput cell experiments. The tool is most often used for processing data through the following pipeline: -Description of the pycytominer pipeline. Images flow from feature extraction and are processed with a series of steps +Description of the pycytominer pipeline. Images flow from feature extraction and are processed with a series of steps > Figure 1. The standard image-based profiling experiment and the role of Pycytominer. (A) In the experimental phase, a scientist plates cells, often perturbing them with chemical or genetic agents and performs microscopy imaging. In image analysis, using CellProfiler for example, a scientist applies several data processing steps to generate image-based profiles. In addition, scientists can apply a more flexible approach by using deep learning models, such as DeepProfiler, to generate image-based profiles. (B) Pycytominer performs image-based profiling to process morphology features and make them ready for downstream analyses. (C) Pycytominer performs five fundamental functions, each implemented with a simple and intuitive API. Each function enables a user to implement various methods for executing operations. From 78d2d14cfce0b3dbd62892c6d55151c85965ec68 Mon Sep 17 00:00:00 2001 From: axiomcura Date: Thu, 26 Sep 2024 14:10:30 -0600 Subject: [PATCH 28/33] added references and updated docs --- README.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 44553e47..0e0e0e8b 100644 --- a/README.md +++ b/README.md @@ -17,12 +17,13 @@ The tool is most often used for processing data through the following pipeline: [Click here for high resolution pipeline image]("./media/pipeline.png) -Image data flow from a microscope to cell segmentation and feature extraction tools (e.g. CellProfiler or DeepProfiler). +Image data flow from a microscope to cell segmentation and feature extraction tools (e.g. [CellProfiler](https://cellprofiler.org/) or [DeepProfiler](https://cytomining.github.io/DeepProfiler-handbook/docs/00-welcome.html)). From here, additional single cell processing tools curate the single cell readouts into a form manageable for pycytominer input. For CellProfiler, we use [cytominer-database](https://github.com/cytomining/cytominer-database) or [CytoTable](https://github.com/cytomining/CytoTable). For DeepProfiler, we include single cell processing tools in [pycytominer.cyto_utils](pycytominer/cyto_utils/). -From the single cell output, pycytominer performs five steps using a simple API (described below), before passing along data to [cytominer-eval](https://github.com/cytomining/cytominer-eval) for quality and perturbation strength evaluation. +The Pycytominer API consists of five key steps applied to the single-cell output (outlined above). +The outputs generated by Pycytominer are subsequently utilized for downstream analysis, which includes machine learning models and statistical testing to derive insights. The best way to communicate with us is through [GitHub Issues](https://github.com/cytomining/pycytominer/issues), where we are able to discuss and troubleshoot topics related to pycytominer. Please see our [`CONTRIBUTING.md`](https://github.com/cytomining/pycytominer/blob/main/CONTRIBUTING.md) for details about communicating possible bugs, new features, or other information. From be6621ec72a936ab9bea09404f6fbfd8d67b44b6 Mon Sep 17 00:00:00 2001 From: axiomcura Date: Thu, 26 Sep 2024 14:13:07 -0600 Subject: [PATCH 29/33] added references --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 0e0e0e8b..a27075ea 100644 --- a/README.md +++ b/README.md @@ -19,11 +19,11 @@ The tool is most often used for processing data through the following pipeline: Image data flow from a microscope to cell segmentation and feature extraction tools (e.g. [CellProfiler](https://cellprofiler.org/) or [DeepProfiler](https://cytomining.github.io/DeepProfiler-handbook/docs/00-welcome.html)). From here, additional single cell processing tools curate the single cell readouts into a form manageable for pycytominer input. -For CellProfiler, we use [cytominer-database](https://github.com/cytomining/cytominer-database) or [CytoTable](https://github.com/cytomining/CytoTable). -For DeepProfiler, we include single cell processing tools in [pycytominer.cyto_utils](pycytominer/cyto_utils/). +For [CellProfiler](https://cellprofiler.org/), we use [cytominer-database](https://github.com/cytomining/cytominer-database) or [CytoTable](https://github.com/cytomining/CytoTable). +For [DeepProfiler](https://cytomining.github.io/DeepProfiler-handbook/docs/00-welcome.html), we include single cell processing tools in [pycytominer.cyto_utils](pycytominer/cyto_utils/). The Pycytominer API consists of five key steps applied to the single-cell output (outlined above). -The outputs generated by Pycytominer are subsequently utilized for downstream analysis, which includes machine learning models and statistical testing to derive insights. +The outputs generated by Pycytominer are utilized for downstream analysis, which includes machine learning models and statistical testing to derive insights. The best way to communicate with us is through [GitHub Issues](https://github.com/cytomining/pycytominer/issues), where we are able to discuss and troubleshoot topics related to pycytominer. Please see our [`CONTRIBUTING.md`](https://github.com/cytomining/pycytominer/blob/main/CONTRIBUTING.md) for details about communicating possible bugs, new features, or other information. From f85a2a278f5c0bebff30fb9661cc1a0fea8bf064 Mon Sep 17 00:00:00 2001 From: Erik Serrano <31600622+axiomcura@users.noreply.github.com> Date: Thu, 26 Sep 2024 15:11:19 -0600 Subject: [PATCH 30/33] Update README.md Co-authored-by: Gregory Way --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index a27075ea..1c756b9d 100644 --- a/README.md +++ b/README.md @@ -22,7 +22,8 @@ From here, additional single cell processing tools curate the single cell readou For [CellProfiler](https://cellprofiler.org/), we use [cytominer-database](https://github.com/cytomining/cytominer-database) or [CytoTable](https://github.com/cytomining/CytoTable). For [DeepProfiler](https://cytomining.github.io/DeepProfiler-handbook/docs/00-welcome.html), we include single cell processing tools in [pycytominer.cyto_utils](pycytominer/cyto_utils/). -The Pycytominer API consists of five key steps applied to the single-cell output (outlined above). +Next, Pycytominer performs reproducible image-based profiling (**Figure 1B**). +The Pycytominer API consists of five key steps (**Figure 1C**). The outputs generated by Pycytominer are utilized for downstream analysis, which includes machine learning models and statistical testing to derive insights. The best way to communicate with us is through [GitHub Issues](https://github.com/cytomining/pycytominer/issues), where we are able to discuss and troubleshoot topics related to pycytominer. From 7820fc265e53df5907ad5f1220ef8e61c3a812df Mon Sep 17 00:00:00 2001 From: axiomcura Date: Thu, 26 Sep 2024 15:16:35 -0600 Subject: [PATCH 31/33] added full links --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 1c756b9d..1de3bf51 100644 --- a/README.md +++ b/README.md @@ -11,11 +11,11 @@ Pycytominer is a suite of common functions used to process high dimensional readouts from high-throughput cell experiments. The tool is most often used for processing data through the following pipeline: -Description of the pycytominer pipeline. Images flow from feature extraction and are processed with a series of steps +Description of the pycytominer pipeline. Images flow from feature extraction and are processed with a series of steps > Figure 1. The standard image-based profiling experiment and the role of Pycytominer. (A) In the experimental phase, a scientist plates cells, often perturbing them with chemical or genetic agents and performs microscopy imaging. In image analysis, using CellProfiler for example, a scientist applies several data processing steps to generate image-based profiles. In addition, scientists can apply a more flexible approach by using deep learning models, such as DeepProfiler, to generate image-based profiles. (B) Pycytominer performs image-based profiling to process morphology features and make them ready for downstream analyses. (C) Pycytominer performs five fundamental functions, each implemented with a simple and intuitive API. Each function enables a user to implement various methods for executing operations. -[Click here for high resolution pipeline image]("./media/pipeline.png) +[Click here for high resolution pipeline image]("https://github.com/cytomining/pycytominer/blob/main/media/pipeline.png") Image data flow from a microscope to cell segmentation and feature extraction tools (e.g. [CellProfiler](https://cellprofiler.org/) or [DeepProfiler](https://cytomining.github.io/DeepProfiler-handbook/docs/00-welcome.html)). From here, additional single cell processing tools curate the single cell readouts into a form manageable for pycytominer input. From f2caf87a377a544d7d598196e34aca0753ad2359 Mon Sep 17 00:00:00 2001 From: Erik Serrano <31600622+axiomcura@users.noreply.github.com> Date: Thu, 26 Sep 2024 15:17:54 -0600 Subject: [PATCH 32/33] Update README.md Co-authored-by: Gregory Way --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 1de3bf51..a1dade28 100644 --- a/README.md +++ b/README.md @@ -17,7 +17,7 @@ The tool is most often used for processing data through the following pipeline: [Click here for high resolution pipeline image]("https://github.com/cytomining/pycytominer/blob/main/media/pipeline.png") -Image data flow from a microscope to cell segmentation and feature extraction tools (e.g. [CellProfiler](https://cellprofiler.org/) or [DeepProfiler](https://cytomining.github.io/DeepProfiler-handbook/docs/00-welcome.html)). +Image data flow from a microscope to cell segmentation and feature extraction tools (e.g. [CellProfiler](https://cellprofiler.org/) or [DeepProfiler](https://cytomining.github.io/DeepProfiler-handbook/docs/00-welcome.html)) (**Figure 1A**). From here, additional single cell processing tools curate the single cell readouts into a form manageable for pycytominer input. For [CellProfiler](https://cellprofiler.org/), we use [cytominer-database](https://github.com/cytomining/cytominer-database) or [CytoTable](https://github.com/cytomining/CytoTable). For [DeepProfiler](https://cytomining.github.io/DeepProfiler-handbook/docs/00-welcome.html), we include single cell processing tools in [pycytominer.cyto_utils](pycytominer/cyto_utils/). From 6caab110b97785d0b2053f835fc6f3233c3996b3 Mon Sep 17 00:00:00 2001 From: Erik Serrano <31600622+axiomcura@users.noreply.github.com> Date: Thu, 26 Sep 2024 15:18:01 -0600 Subject: [PATCH 33/33] Update README.md Co-authored-by: Gregory Way --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index a1dade28..c633d6ec 100644 --- a/README.md +++ b/README.md @@ -24,7 +24,7 @@ For [DeepProfiler](https://cytomining.github.io/DeepProfiler-handbook/docs/00-we Next, Pycytominer performs reproducible image-based profiling (**Figure 1B**). The Pycytominer API consists of five key steps (**Figure 1C**). -The outputs generated by Pycytominer are utilized for downstream analysis, which includes machine learning models and statistical testing to derive insights. +The outputs generated by Pycytominer are utilized for downstream analysis, which includes machine learning models and statistical testing to derive biological insights. The best way to communicate with us is through [GitHub Issues](https://github.com/cytomining/pycytominer/issues), where we are able to discuss and troubleshoot topics related to pycytominer. Please see our [`CONTRIBUTING.md`](https://github.com/cytomining/pycytominer/blob/main/CONTRIBUTING.md) for details about communicating possible bugs, new features, or other information.

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