Removed excess from 05.1

scripts/05_Filter_Merged_Marker.py

@@ -138,48 +138,14 @@
                                             merged_filtered_markers["not_crowding"] &
                                             ~merged_filtered_markers["small_coef_in_query"])
 merged_filtered_markers = merged_filtered_markers.sort_values(['Major_Name', 'Queried_Name']) # For now
-merged_filtered_markers.to_csv('05_Filter_Merged_Markers/5_merged_markers_coefficientLengthExpression.txt', sep = '\t')
+merged_filtered_markers.to_csv('05_Filter_Merged_Markers/5_merged_markers_xeniumAnnotated.txt', sep = '\t')
 merged_filtered_markers = merged_filtered_markers.loc[filtered_indices]
-
-merged_filtered_markers.to_csv('05_Filter_Merged_Markers/5_merged_markers_coefficientLengthExpression_Filtered.txt', sep = '\t')
-# sort_values(['Queried_Major_Name', 'Queried_Name'])
+merged_filtered_markers.to_csv('05_Filter_Merged_Markers/5_merged_markers_xeniumFiltered.txt', sep = '\t')
 
 # 05.2 # This is just looking at curated, xenium-filtered markers
 # merged_filtered_markers = pd.read_csv('05_Filter_Merged_Markers/5_curated_markers_annotatedKeep_lengthExpressionMissingFiltered.txt', sep ='\t')
 # merged_filtered_markers = merged_filtered_markers.sort_values(['Queried_Major_Name', 'Queried_Name']) # For now
 
-# 05.1
-merged_filtered_markers = merged_filtered_markers.loc[merged_filtered_markers["Curated"] == "Curated"]
-
-major_detectable = np.sum(merged_filtered_markers.loc[:, 'AC':'Rod'] >= 4, axis = 1) > 0
-major_not_crowding = np.sum(merged_filtered_markers.loc[:, 'AC':'Rod'] > 100, axis = 1) == 0
-merged_filtered_markers["major_keep"] = long_enough & (major_detectable & major_not_crowding)
-merged_filtered_markers["is_major"] = merged_filtered_markers["Queried_Name"].isin(adata.obs["majorclass"].drop_duplicates())
-
-minorToMajor = adata.obs[["author_cell_type", "majorclass"]].drop_duplicates()
-subcell_raw_mean.index = subcell_raw_mean['author_cell_type']
-
-minor_detectable = np.zeros(sum(merged_filtered_markers["Marker"].isin(subcell_raw_mean.columns)))
-minor_not_crowding = np.zeros(sum(merged_filtered_markers["Marker"].isin(subcell_raw_mean.columns)))
-for majorclass in ["AC", "BC", "Microglia", "RGC"]:
-    sub_expr_mtx = subcell_raw_mean.loc[minorToMajor.loc[minorToMajor["majorclass"] == majorclass, "author_cell_type"], merged_filtered_markers.loc[merged_filtered_markers["Marker"].isin(subcell_raw_mean.columns), "Marker"]]
-    minor_detectable = minor_detectable | (np.sum(sub_expr_mtx >= 4, axis = 0) > 0)
-    minor_not_crowding = minor_not_crowding | (np.sum(sub_expr_mtx > 100, axis = 0) == 0)
-
-minor_keep = minor_detectable & minor_not_crowding
-minor_keep.name = "minor_keep"
-merged_filtered_markers.index = merged_filtered_markers.Marker
-merged_filtered_markers = merged_filtered_markers.join(minor_keep, how = "left").drop_duplicates()
-merged_filtered_markers["final_keep"] = (merged_filtered_markers["minor_keep"] & (~merged_filtered_markers["is_major"])) | (merged_filtered_markers["major_keep"] & merged_filtered_markers["is_major"])
-merged_filtered_markers.to_csv('05_Filter_Merged_Markers/5_curated_markers_xeniumAnnotated.txt', sep ='\t', index = False)
-
-not_in_data = merged_filtered_markers["feature_length"] == 0
-not_in_data.index = merged_filtered_markers.index
-merged_filtered_markers["final_keep"] = ((merged_filtered_markers["minor_keep"] & (~merged_filtered_markers["is_major"])) | (merged_filtered_markers["major_keep"] & merged_filtered_markers["is_major"])) & ~not_in_data
-merged_filtered_markers_filtered = merged_filtered_markers.loc[merged_filtered_markers["final_keep"]]
-merged_filtered_markers_filtered.to_csv('05_Filter_Merged_Markers/5_curated_markers_xeniumFiltered.txt', sep ='\t', index = False)
-
-
 def clean_markers(var_names, dirty_markers, verbose=True):
     final_markers = []
     for m in dirty_markers: