fix genoAnnotate code + bump patch version

genomap/genoAnnotate/genoAnnotate.py

@@ -1,92 +1,94 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Sun Jul 23 15:18:43 2023
-
-@author: Md Tauhidul Islam
-# This code is inspired by scType (https://github.com/IanevskiAleksandr/sc-type)
-# We are in the process of using image matching techique for further enhancement
-# of the cell annotation
-"""
-
-from genomap.genotype import *
-import scanpy as sc
-
-def genoAnnotate(adata,tissue_type,database=None):
-    # Input: adata: annData containing the raw gene counts
-    # tissue type: e.g. Immune system,Pancreas,Liver,Eye,Kidney,Brain,Lung,Adrenal,Heart,Intestine,Muscle,Placenta,Spleen,Stomach,Thymus 
-    # database: User can select his/her own database in excel format
-
-    # Database file
-    if database==None:
-        database = "https://raw.githubusercontent.com/xinglab-ai/self-consistent-expression-recovery-machine/master/demo/data/genoANN_db.xlsx";        
-
-    # Filter cells
-    sc.pp.filter_cells(adata, min_genes=200)
-    # Normalize data
-    adata.raw = adata
-    sc.pp.normalize_total(adata, target_sum=10000)
-    sc.pp.log1p(adata)
-    sc.pp.highly_variable_genes(adata, n_top_genes=2000)
-    adata = adata[:, adata.var['highly_variable']]
-    # Scale data and run PCA
-    sc.pp.scale(adata, max_value=10)
-    sc.tl.pca(adata)
-
-    # Prepare positive and negative gene sets
-    result = gene_sets_prepare(database, tissue_type)
-    gs = result['gs_positive']
-    gs2 = result['gs_negative']
-    cell_types = result['cell_types']
-
-
-    data=adata.raw.X.toarray()
-    # Get cell-type by cell matrix
-    scRNAseqData = pd.DataFrame(data, index=adata.raw.obs_names, columns=adata.raw.var_names)
-
-    # Compute cell-type score fro each cell
-    es_max = sctype_score(scRNAseqData=scRNAseqData, scaled=True, gs=gs, gs2=gs2, cell_types=cell_types)
-    es_max.columns = cell_types
-    es_max.index = scRNAseqData.index
-
-    # Calculate neighborhood graph of cells (replace 'adata' with your actual AnnData object)
-    sc.pp.neighbors(adata, n_neighbors=10, use_rep='X_pca')
-    # Perform clustering so that cell-type can be assigned to each cluster
-    sc.tl.leiden(adata)
-    # The cluster labels are stored in `adata.obs['louvain']`
-    results = []
-    for cl in adata.obs['leiden'].unique():
-        cells_in_cluster = adata.obs_names[adata.obs['leiden'] == cl]
-        es_max_cl = es_max.loc[cells_in_cluster].sum().sort_values(ascending=False)
-        results.append(pd.DataFrame({
-            'cluster': cl,
-            'type': es_max_cl.index[:1],
-            'scores': es_max_cl.values[:1],
-            'ncells': len(cells_in_cluster)
-            }))
-
-    results = pd.concat(results)
-    results.loc[results['scores'] < results['ncells'] / 4, 'type'] = 'Unknown'
-    results.set_index('cluster', inplace=True)
-    # Assign the cell type labels to the cells in the AnnData object
-    adata.obs['cell_type'] = results.loc[adata.obs['leiden'], 'type'].values
-    return adata
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+# -*- coding: utf-8 -*-
+"""
+Created on Sun Jul 23 15:18:43 2023
+
+@author: Md Tauhidul Islam
+# This code is inspired by scType (https://github.com/IanevskiAleksandr/sc-type)
+# We are in the process of using image matching techique for further enhancement
+# of the cell annotation
+"""
+
+
+from genomap.genotype import *
+import scanpy as sc
+
+def genoAnnotate(adata, species, tissue_type, database=None):
+    # Input: adata: annData containing the raw gene counts
+    # species :'human' or 'mouse'
+    # tissue type: e.g. Immune system,Pancreas,Liver,Eye,Kidney,Brain,Lung,Adrenal,Heart,Intestine,Muscle,Placenta,Spleen,Stomach,Thymus 
+    # database: User can select his/her own database in excel format
+
+    # Database file
+    if database==None:
+        database = "https://raw.githubusercontent.com/xinglab-ai/self-consistent-expression-recovery-machine/master/demo/data/genoANN_db.xlsx";        
+
+    # Filter cells
+    sc.pp.filter_cells(adata, min_genes=200)
+    # Normalize data
+    adata.raw = adata
+    sc.pp.normalize_total(adata, target_sum=10000)
+    sc.pp.log1p(adata)
+    sc.pp.highly_variable_genes(adata, n_top_genes=2000)
+    adata = adata[:, adata.var['highly_variable']]
+    # Scale data and run PCA
+    sc.pp.scale(adata, max_value=10)
+    sc.tl.pca(adata)
+
+    # Prepare positive and negative gene sets
+    result = gene_sets_prepare(database, tissue_type, species)
+    gs = result['gs_positive']
+    gs2 = result['gs_negative']
+    cell_types = result['cell_types']
+
+
+    data=adata.raw.X.toarray()
+    # Get cell-type by cell matrix
+    scRNAseqData = pd.DataFrame(data, index=adata.raw.obs_names, columns=adata.raw.var_names)
+
+    # Compute cell-type score fro each cell
+    es_max = sctype_score(scRNAseqData=scRNAseqData, scaled=True, gs=gs, gs2=gs2, cell_types=cell_types)
+    es_max.columns = cell_types
+    es_max.index = scRNAseqData.index
+
+    # Calculate neighborhood graph of cells (replace 'adata' with your actual AnnData object)
+    sc.pp.neighbors(adata, n_neighbors=10, use_rep='X_pca')
+    # Perform clustering so that cell-type can be assigned to each cluster
+    sc.tl.leiden(adata)
+    # The cluster labels are stored in `adata.obs['leiden']`
+    results = []
+    for cl in adata.obs['leiden'].unique():
+        cells_in_cluster = adata.obs_names[adata.obs['leiden'] == cl]
+        es_max_cl = es_max.loc[cells_in_cluster].sum().sort_values(ascending=False)
+        results.append(pd.DataFrame({
+            'cluster': cl,
+            'type': es_max_cl.index[:1],
+            'scores': es_max_cl.values[:1],
+            'ncells': len(cells_in_cluster)
+            }))
+
+    results = pd.concat(results)
+    results.loc[results['scores'] < results['ncells'] / 4, 'type'] = 'Unknown'
+    results.set_index('cluster', inplace=True)
+    # Assign the cell type labels to the cells in the AnnData object
+    adata.obs['cell_type'] = results.loc[adata.obs['leiden'], 'type'].values
+    return adata
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