update structure plot

Topyfic/analysis.py

@@ -180,6 +180,7 @@ def structure_plot(self,
                        width=None,
                        n=2,
                        order_cells=['hierarchy'],
+                       subsample=10000,
                        save=True,
                        show=True,
                        figsize=None,
@@ -196,7 +197,7 @@ def structure_plot(self,
         :type topic_order: list of str
         :param ascending: for each structure plot on which order you want to sort your data (default is descending for all structure plot)
         :type ascending: list of bool
-        :param metaData: if you want to add annotation for each cell add column name of that information (make sure you have that inforamtion in your cell_participation.obs)
+        :param metaData: if you want to add annotation for each cell add column name of that information (make sure you have that information in your cell_participation.obs)
         :type metaData: list
         :param metaData_palette: color palette for each metaData you add
         :type metaData_palette: dict
@@ -206,6 +207,8 @@ def structure_plot(self,
         :type n: int
         :param order_cells: determine which kind of sorting options you want to use ('sum', 'hierarchy', sort by metaData); sum: sort cells by sum of top n topics; hierarchy: sort data by doing hierarchical clustring; metaData sort by metaData (default: ['hierarchy'])
         :type order_cells: list
+        :param subsample: number of cells/nuclei to subsample the data to be able to render it in a reasonable time (default: 10000)
+        :type subsample: int
         :param save: indicate if you want to save the plot or not (default: True)
         :type save: bool
         :param show: indicate if you want to show the plot or not (default: True)
@@ -217,6 +220,7 @@ def structure_plot(self,
         :param file_name: name and path of the plot use for save (default: piechart_topicAvgCell)
         :type file_name: str
         """
+
         if category is None:
             category = self.cell_participation.obs[level].unique().tolist()
         if figsize is None:
@@ -231,7 +235,11 @@ def structure_plot(self,
 
         a = []
         for i in range(len(category)):
-            a.append(self.cell_participation.obs[self.cell_participation.obs[level] == category[i]].shape[0])
+            n_cells = self.cell_participation.obs[self.cell_participation.obs[level] == category[i]].shape[0]
+            if n_cells > subsample:
+                a.append(subsample)
+            else:
+                a.append(n_cells)
         a.append(min(a) / 2)
         if width is None:
             width = a
@@ -258,6 +266,14 @@ def structure_plot(self,
         for i in range(len(category)):
             tissue = self.cell_participation.obs[self.cell_participation.obs[level] == category[i]]
             tmp = self.cell_participation.to_df().loc[tissue.index, :]
+
+            if tmp.shape[0] > subsample:
+                print(f"Randomly choose {subsample} out of {tmp.shape[0]} cells/nuclei in {category[i]}")
+                subsample_index = random.sample(list(range(tmp.shape[0])), subsample)
+
+                tmp = tmp.iloc[subsample_index, :]
+                tissue = tissue.iloc[subsample_index, :]
+
             if topic_order is None:
                 order = tmp.mean().sort_values(ascending=False).index.tolist()
             else:

setup.py

@@ -3,15 +3,15 @@
 setup(
     name='Topyfic',  # the name of your package
     packages=['Topyfic'],  # same as above
-    version='v0.4.16',  # version number
+    version='v0.4.17',  # version number
     license='MIT',  # license type
     description='Topyfic is a Python package designed to identify reproducible latent dirichlet allocation (LDA) '
                 'using leiden clustering and harmony for single cell epigenomics data',
     # short description
     author='Narges Rezaie',  # your name
     author_email='[email protected]',  # your email
     url='https://github.com/mortazavilab/Topyfic',  # url to your git repo
-    download_url='https://github.com/mortazavilab/Topyfic/archive/refs/tags/v0.4.16.tar.gz',  # link to the tar.gz file associated with this release
+    download_url='https://github.com/mortazavilab/Topyfic/archive/refs/tags/v0.4.17.tar.gz',  # link to the tar.gz file associated with this release
     keywords=['Cellular Programs', 'Latent Dirichlet allocation', 'single-cell multiome', 'single-cell RNA-seq',
               'gene regulatory network', 'Topic Modeling', 'single-nucleus RNA-seq'],  #
     python_requires='>=3.9',