Enrichr links; References; Clustergrammer

Author: maxim-k

appyters/Bulk_RNA_seq/RNA_seq_Analysis_Pipeline.ipynb

@@ -815,6 +815,7 @@
     "    enrichr_link_dict[title_down] = dict()\n",
     "    enrichr_link_dict[title_down][\"link\"] = \"<a href=https://maayanlab.cloud/Enrichr/enrich?dataset={} target=\\\"_blank\\\">link to Enrichr</a>\".format(results['enrichr'][label][\"downregulated\"][\"shortId\"])\n",
     "\n",
+    "notebook_metadata['enrichr_links'] = enrichr_link_dict\n",
     "enrichr_link_df = pd.DataFrame.from_dict(enrichr_link_dict).T\n",
     "table_counter, notebook_metadata = display_object(table_counter, \"The table displays links to Enrichr containing the results of enrichment analyses generated by analyzing the up-regulated and down-regulated genes from a differential expression analysis. By clicking on these links, users can interactively explore and download the enrichment results from the Enrichr website.\", notebook_metadata=notebook_metadata, saved_filename=\"enrichr_links.csv\", df=enrichr_link_df, ishtml=True)"
    ]

appyters/Bulk_RNA_seq/appyter.json

@@ -2,14 +2,18 @@
   "$schema": "https://raw.githubusercontent.com/MaayanLab/appyter-catalog/master/schema/appyter-validator.json",
   "name": "Bulk_RNA_seq",
   "title": "Bulk RNA-seq analysis pipeline",
-  "version": "0.1.4",
+  "version": "0.1.5",
   "description": "Bulk RNA-seq analysis pipeline enables you to analyze and visualize datasets with state-of-the-art algorithms and visualization methods.",
   "image": "screenshot.png",
   "authors": [
     {
       "name": "Minji Jeon",
       "email": "minji.jeon1@gmail.com"
-    }
+    },
+    {
+      "name": "Maxim Kuleshov",
+      "email": "maxim.kuleshov@mssm.edu"
+    }    
   ],
   "url": "https://github.com/MaayanLab/appyter-catalog",
   "tags": [

appyters/Bulk_RNA_seq/utils.py

@@ -923,7 +923,7 @@ def plot_l1000cds2(l1000cds2_results, counter, notebook_metadata, nr_drugs=7, he
 
     else:
         # Bar charts
-        fig = tools.make_subplots(rows=1, cols=2, print_grid=False)
+        fig = tools.make_subplots(rows=1, cols=2, print_grid=False);
         for i, direction in enumerate(['mimic', 'reverse']):
             drug_counts = l1000cds2_results[direction]['table'].groupby('pert_desc').size().sort_values(
                 ascending=False).iloc[:nr_drugs].iloc[::-1]
@@ -975,14 +975,15 @@ def plot_l1000cds2(l1000cds2_results, counter, notebook_metadata, nr_drugs=7, he
                                                     notebook_metadata, saved_filename=plot_name, istable=False)
         notebook_metadata["figures"][str(counter - 1)]["file_desc"] = [
             {"name": "Mimic Signature Query Results", "link": l1000cds2_results['mimic']['url']},
+            {"name": "Reverse Signature Query Results", "link": l1000cds2_results['reverse']['url']}, ]
 
-            # Links
-            display(Markdown(' *Mimic Signature Query Results*:'))
-            display_link(l1000cds2_results['mimic']['url'])
-            display(Markdown(' *Reverse Signature Query Results*:'))
-            display_link(l1000cds2_results['reverse']['url'])
+        # Links
+        display(Markdown(' *Mimic Signature Query Results*:'))
+        display_link(l1000cds2_results['mimic']['url'])
+        display(Markdown(' *Reverse Signature Query Results*:'))
+        display_link(l1000cds2_results['reverse']['url'])
 
-        return counter, notebook_metadata
+    return counter, notebook_metadata
 
 
 def run_l1000fwd(signature, nr_genes=500, signature_label=''):