zero out tf counts

app.py

@@ -278,12 +278,17 @@ def get_heatmaps(gene, locus1, locus2, heatmap_range):
 
 
 def perturb(gene, locus1, locus2, use_max_gene, tf_knockout):
+    in_rna = rna.copy()
     if tf_knockout:
         guidance_hvf = prior.copy()
         try:
             guidance_hvf.remove_node(gene)
         except:
             pass
+        # find index of tf and set all counts and values to zero
+        gene_idx = gene_idx_map[gene]
+        in_rna.layers['counts'][:, gene_idx] = 0
+        in_rna.X[:, gene_idx] = 0
     else:
         locus1 = locus1.replace(',', '')
         locus2 = locus2.replace(',', '')
@@ -356,16 +361,18 @@ def perturb(gene, locus1, locus2, use_max_gene, tf_knockout):
             guidance_hvf.remove_edge(a2_closest_gene, a2)
         except:
             pass
-
-    perterbed_rna_recon = glue.decode_data("rna", "rna", rna, guidance_hvf)
+    
+    perterbed_rna_recon = glue.decode_data("rna", "rna", in_rna, guidance_hvf)
     ism = np.log2((perterbed_rna_recon + eps) / (rna_recon + eps))
-    max_gene_idxs = np.abs(np.mean(ism, axis=0)).argsort()[::-1][:10]
-    max_gene_idx = np.abs(np.mean(ism, axis=0)).argmax()
+    if tf_knockout:
+        max_gene_idxs = np.abs(np.mean(ism, axis=0)).argsort()[::-1][:10]
+        max_gene_idx = max_gene_idxs[1]
+    else:
+        max_gene_idx = np.abs(np.mean(ism, axis=0)).argmax()
     max_gene = rna.var.query(f"highly_variable").index[max_gene_idx]
     print('Max gene:', max_gene)
     for max_gene_idx in max_gene_idxs:
-        max_gene = rna.var.query(f"highly_variable").index[max_gene_idx]
-        print(max_gene, np.mean(ism[:, max_gene_idx]))
+        print(rna.var.query(f"highly_variable").index[max_gene_idx], np.mean(ism[:, max_gene_idx]))
     # get integer index of gene
     if use_max_gene:
         gene_idx = gene_idx_map[max_gene]