notebooks_mod2

theislab · Dec 18, 2024 · 8c0b035 · 8c0b035
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diff --git a/notebooks/spatialdata_tutorials/10_apply_sc_signatures_to_exrna.ipynb b/notebooks/spatialdata_tutorials/10_apply_sc_signatures_to_exrna.ipynb
diff --git a/notebooks/spatialdata_tutorials/1_run_segmentation_free.ipynb b/notebooks/spatialdata_tutorials/1_run_segmentation_free.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -12,7 +12,7 @@
     "import spatialdata_plot\n",
     "import sys\n",
     "sys.path.append(\"../../src\")\n",
-    "import troutpy \n"
+    "import troutpy "
    ]
   },
   {
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -84,7 +84,7 @@
     "#define points2regions_params\n",
     "points2regions_params={'num_clusters':100,  'pixel_width':0.4,  'pixel_smoothing':3.5}\n",
     "#run_segmentation_free\n",
-    "exrna.tl.segmentation_free_clustering(sdata, params=points2regions_params, \n",
+    "troutpy.tl.segmentation_free_clustering(sdata, params=points2regions_params, \n",
     "    x='x',  y='y',  feature_name='feature_name',  transcript_id='transcript_id')"
    ]
   },
@@ -97,7 +97,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -110,7 +110,7 @@
     }
    ],
    "source": [
-    "exrna.pp.define_extracellular(sdata, method='segmentation_free',min_prop_of_extracellular=0.8) "
+    "troutpy.pp.define_extracellular(sdata, method='segmentation_free',min_prop_of_extracellular=0.8) "
    ]
   },
   {
@@ -190,7 +190,7 @@
     "\n",
     "\n",
     "input_data=sdata.points['transcripts'][['missegmentation_associated']].compute()\n",
-    "exrna.pl.pie_of_positive(input_data,groupby='missegmentation_associated',figures_path=output_path,save=True)"
+    "troutpy.pl.pie_of_positive(input_data,groupby='missegmentation_associated',figures_path=output_path,save=True)"
    ]
   }
  ],

diff --git a/notebooks/spatialdata_tutorials/3_quantify_exRNA.ipynb b/notebooks/spatialdata_tutorials/3_quantify_exRNA.ipynb
@@ -80,7 +80,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -111,7 +111,7 @@
     "control_codewords = ['negative_control_probe','unassigned_codeword', 'deprecated_codeword', \n",
     "                     'genomic_control_probe', 'negative_control_codeword']\n",
     "\n",
-    "exrna.tl.quantify_overexpression(sdata,layer='transcripts',codeword_column=\"codeword_category\",\n",
+    "troutpy.tl.quantify_overexpression(sdata,layer='transcripts',codeword_column=\"codeword_category\",\n",
     "    control_codewords=control_codewords,gene_id_column=\"feature_name\",percentile_threshold=99.99)\n"
    ]
   },
@@ -124,7 +124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -137,7 +137,7 @@
     }
    ],
    "source": [
-    "exrna.tl.extracellular_enrichment(sdata)"
+    "troutpy.tl.extracellular_enrichment(sdata)"
    ]
   },
   {
@@ -149,7 +149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -161,7 +161,7 @@
     }
    ],
    "source": [
-    "exrna.tl.spatial_variability(sdata,  gene_id_key='feature_name', n_neighbors=10,binsize=20)"
+    "troutpy.tl.spatial_variability(sdata,  gene_id_key='feature_name', n_neighbors=10,binsize=20)"
    ]
   },
   {
@@ -173,7 +173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -185,7 +185,7 @@
     }
    ],
    "source": [
-    "exrna.tl.spatial_colocalization(sdata, coords_keys=['x', 'y'], gene_id_key='feature_name',resolution=1000,binsize=5, threshold_colocalized=1 ,copy=False)"
+    "troutpy.tl.spatial_colocalization(sdata, coords_keys=['x', 'y'], gene_id_key='feature_name',resolution=1000,binsize=5, threshold_colocalized=1 ,copy=False)"
    ]
   },
   {

diff --git a/notebooks/spatialdata_tutorials/4_explore_comunication.ipynb b/notebooks/spatialdata_tutorials/4_explore_comunication.ipynb
@@ -74,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -87,20 +87,20 @@
     }
    ],
    "source": [
-    "exrna.tl.compute_source_cells(sdata, expression_threshold=0)"
+    "troutpy.tl.compute_source_cells(sdata, expression_threshold=0)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# work on visualization\n",
     "#filtered_proportions = source_proportion[(source_proportion > 0.1).any(axis=1)]\n",
     "#absent_source_proportions = source_proportion[np.max(source_proportion,axis=1)<0.1]\n",
-    "#exrna.pl.sorted_heatmap(source_proportion, output_path,filename=\"Heatmap_source_cells_by_gene.pdf\",cmap='Blues',vmax=0.2)\n",
-    "#exrna.pl.sorted_heatmap(absent_source_proportions, output_path,filename=\"Heatmap_source_cells_by_gene.pdf\",cmap='Blues',vmax=1,figsize=(4,3))"
+    "#troutpy.pl.sorted_heatmap(source_proportion, output_path,filename=\"Heatmap_source_cells_by_gene.pdf\",cmap='Blues',vmax=0.2)\n",
+    "#troutpy.pl.sorted_heatmap(absent_source_proportions, output_path,filename=\"Heatmap_source_cells_by_gene.pdf\",cmap='Blues',vmax=1,figsize=(4,3))"
    ]
   },
   {
@@ -124,7 +124,7 @@
     }
    ],
    "source": [
-    "exrna.tl.distance_to_source_cell(sdata, xcellcoord='x_centroid', ycellcoord='y_centroid',gene_id_column='feature_name')"
+    "troutpy.tl.distance_to_source_cell(sdata, xcellcoord='x_centroid', ycellcoord='y_centroid',gene_id_column='feature_name')"
    ]
   },
   {
@@ -142,7 +142,7 @@
     }
    ],
    "source": [
-    "exrna.tl.compute_distant_cells_prop(sdata, layer='transcripts', gene_id_column='feature_name', threshold=30,copy=False)"
+    "troutpy.tl.compute_distant_cells_prop(sdata, layer='transcripts', gene_id_column='feature_name', threshold=30,copy=False)"
    ]
   },
   {
@@ -152,7 +152,7 @@
    "outputs": [],
    "source": [
     "## work on plotting\n",
-    "#exrna.pl.proportion_above_threshold(proportions_above_threshold, top_percentile=0.01, bottom_percentile=0.01, figsize=(5, 10), bar_color=\"orange\",save=True,output_path=output_path)"
+    "#troutpy.pl.proportion_above_threshold(proportions_above_threshold, top_percentile=0.01, bottom_percentile=0.01, figsize=(5, 10), bar_color=\"orange\",save=True,output_path=output_path)"
    ]
   },
   {
@@ -181,7 +181,7 @@
    ],
    "source": [
     "# Calculate closest cells and distances\n",
-    "exrna.tl.calculate_target_cells(sdata, layer='transcripts', xcoord='x', \n",
+    "troutpy.tl.calculate_target_cells(sdata, layer='transcripts', xcoord='x', \n",
     "ycoord='y',xcellcoord='x_centroid',ycellcoord='y_centroid',celltype_key='cell type',copy=False)"
    ]
   },
@@ -192,8 +192,8 @@
    "outputs": [],
    "source": [
     "### work on plotting \n",
-    "##exrna.pl.sorted_heatmap(target_proportion, output_path,filename=\"Heatmap_target_cells_by_gene.pdf\",cmap='Reds',vmax=1)\n",
-    "#exrna.pl.coupled_scatter(sdata,layer='extracellular_transcripts_enriched',save=False,transcript_group='distance_to_source_cell',size=3,vmax=40,figsize=(6,4))"
+    "##troutpy.pl.sorted_heatmap(target_proportion, output_path,filename=\"Heatmap_target_cells_by_gene.pdf\",cmap='Reds',vmax=1)\n",
+    "#troutpy.pl.coupled_scatter(sdata,layer='extracellular_transcripts_enriched',save=False,transcript_group='distance_to_source_cell',size=3,vmax=40,figsize=(6,4))"
    ]
   },
   {
@@ -304,7 +304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -319,7 +319,7 @@
     }
    ],
    "source": [
-    "exrna.pl.sorted_heatmap(communications_ngenes.astype(float),save=False,figsize=(5,5))"
+    "troutpy.pl.sorted_heatmap(communications_ngenes.astype(float),save=False,figsize=(5,5))"
    ]
   },
   {
@@ -340,7 +340,7 @@
    ],
    "source": [
     "# Is this a plotting function?\n",
-    "interactions_Vxn=exrna.tl.get_gene_interaction_strength(source_proportion, target_proportion,gene_symbol=\"Gfap\")"
+    "interactions_Vxn=troutpy.tl.get_gene_interaction_strength(source_proportion, target_proportion,gene_symbol=\"Gfap\")"
    ]
   },
   {
@@ -368,7 +368,7 @@
     }
    ],
    "source": [
-    "exrna.pl.spatial_interactions(sdata,layer= 'extracellular_transcripts_enriched', gene = 'Arc',gene_key= 'feature_name',figsize=(5,5))"
+    "troutpy.pl.spatial_interactions(sdata,layer= 'extracellular_transcripts_enriched', gene = 'Arc',gene_key= 'feature_name',figsize=(5,5))"
    ]
   },
   {
@@ -396,7 +396,7 @@
     }
    ],
    "source": [
-    "exrna.pl.interactions_with_arrows(sdata,layer= 'extracellular_transcripts_enriched', gene = 'Kif5a',gene_key= 'feature_name',figsize=(7,7),dpi=100,size=5)"
+    "troutpy.pl.interactions_with_arrows(sdata,layer= 'extracellular_transcripts_enriched', gene = 'Kif5a',gene_key= 'feature_name',figsize=(7,7),dpi=100,size=5)"
    ]
   }
  ],

diff --git a/notebooks/spatialdata_tutorials/6_identify_patterns.ipynb b/notebooks/spatialdata_tutorials/6_identify_patterns.ipynb
@@ -74,11 +74,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "sdata=exrna.tl.nmf(sdata, layer='extracellular_transcripts', feature_key='feature_name', bin_key='bin_id', \n",
+    "sdata=troutpy.tl.nmf(sdata, layer='extracellular_transcripts', feature_key='feature_name', bin_key='bin_id', \n",
     "    density_table_key='segmentation_free_table', n_components=10, subsample_percentage=0.1,all=False)"
    ]
   },
@@ -89,12 +89,12 @@
    "outputs": [],
    "source": [
     "## in this part we plot the factors in exrna\n",
-    "#exrna.pl.nmf_factors_exrna_cells_W(sdata,nmf_adata_key,saving_path=output_path,save=False,spot_size=5) "
+    "#troutpy.pl.nmf_factors_exrna_cells_W(sdata,nmf_adata_key,saving_path=output_path,save=False,spot_size=5) "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -109,7 +109,7 @@
     }
    ],
    "source": [
-    "exrna.pl.nmf_gene_contributions(sdata,nmf_adata_key='nmf_data', vmin=0.0, vmax=0.02,saving_path=output_path,save=False,figsize=(4,5))"
+    "troutpy.pl.nmf_gene_contributions(sdata,nmf_adata_key='nmf_data', vmin=0.0, vmax=0.02,saving_path=output_path,save=False,figsize=(4,5))"
    ]
   },
   {
@@ -136,7 +136,7 @@
     }
    ],
    "source": [
-    "sdata = exrna.tl.apply_exrna_factors_to_cells(sdata,layer_factors='nmf_data')"
+    "sdata = troutpy.tl.apply_exrna_factors_to_cells(sdata,layer_factors='nmf_data')"
    ]
   },
   {
@@ -246,7 +246,7 @@
     }
    ],
    "source": [
-    "exrna.pl.paired_nmf_factors(sdata,figsize=(7,7),n_factors=10)"
+    "troutpy.pl.paired_nmf_factors(sdata,figsize=(7,7),n_factors=10)"
    ]
   }
  ],

diff --git a/notebooks/spatialdata_tutorials/7_image_params.ipynb b/notebooks/spatialdata_tutorials/7_image_params.ipynb
@@ -116,26 +116,6 @@
     "plt.scatter(transcripts.x_scaled,transcripts.y_scaled,s=0.01,color='red')"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(5, 3000, 5000)"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "imarray.shape"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 66,

diff --git a/notebooks/spatialdata_tutorials/8_exrna_signature.ipynb b/notebooks/spatialdata_tutorials/8_exrna_signature.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -26,7 +26,7 @@
     "import seaborn as sns\n",
     "import matplotlib.pyplot as plt\n",
     "import spatialdata_io\n",
-    "import spatialdata as sd\n"
+    "import spatialdata as sd"
    ]
   },
   {