add multi-gene analysis, remove LINCS

appyters/Drug_Gene_Budger2/README.md

@@ -1,11 +1,11 @@
-# Dr. Gene Budger (DGB) 2
+# DrugRanger
 
-The Dr. Gene Budger 2 (DGB2) Appyter takes a single human gene as input, and returns ranked up- and down-regulating drugs from three Connectivity Mapping resources that were shown to maximally increase or decrease the mRNA expression of the gene in human cell lines. The three Connectivity Mapping resources are:
+The DrugRanger Appyter takes a single human gene or gene set as input, and returns ranked up- and down-regulating drugs from three Connectivity Mapping resources that were shown to maximally increase or decrease the mRNA expression of the gene(s) in human cell lines. The three Connectivity Mapping resources are:
 
 - [Ginkgo GDPx1 and GDPx2 datasets](https://huggingface.co/ginkgo-datapoints)
 
 - [Novartis DRUG-seq U2OS MoABox dataset](https://zenodo.org/records/14291446)
 
-- [LINCS L1000 Chemical Perturbation dataset](https://maayanlab.cloud/sigcom-lincs/#/Download)
+- [Tahoe-100M](https://huggingface.co/datasets/tahoebio/Tahoe-100M)
 
-In addition to producing tables of ranked up- and down-regulating drugs of the input gene, the notebook creates volcano plot visualizations and UpSet plots that identify overlap in regulators across datasets. 
+In addition to producing tables of ranked up- and down-regulating drugs of the input gene, the notebook creates various visualizations for the single gene and multi-gene analysis, to help users determine the most effective regulators of their input gene(s). 

appyters/Drug_Gene_Budger2/appyter.json

@@ -1,10 +1,10 @@
 {
     "$schema": "https://raw.githubusercontent.com/MaayanLab/appyter-catalog/main/schema/appyter-validator.json",
     "name": "Drug_Gene_Budger2",
-    "title": "Dr. Gene Budger (DGB) 2",
-    "version": "0.0.8",
-    "description": "An appyter that retrieves drugs that up-regulate and down-regulate a single input gene across Connectivity Mapping datasets",
-    "image": "dgb_logo.png",
+    "title": "DrugRanger",
+    "version": "0.1.0",
+    "description": "An appyter that retrieves drugs that up-regulate and down-regulate a single input gene or gene set across Connectivity Mapping datasets",
+    "image": "DR_logo.png",
     "authors": [
         {
             "name": "Lily Taub",
@@ -13,7 +13,6 @@
     ],
     "url": "https://github.com/MaayanLab/appyter-catalog",
     "tags": [
-        "L1000",
         "DRUG-seq",
         "RNA-seq"
     ],

appyters/Drug_Gene_Budger2/cmap_readers.py

@@ -0,0 +1,136 @@
+import pandas as pd 
+import numpy as np
+import hashlib
+import polars as pl
+
+def prepare_novartis_data(gene, URL = 'https://appyters.maayanlab.cloud/storage/DrugRegulators_Appyter/novartis_de'):
+    '''
+    gene: gene symbol to retrieve
+    URL: Novartis data storage location
+
+    output: results dataframe from Novartis data
+    '''
+    try:
+        novartis_de = pd.read_feather(f'{URL}/{gene}.f').set_index('index')
+    except:
+        # print(f'{gene} not found in Novartis')
+        return None
+     # format p-values
+    novartis_de['log10adj.P.Val'] = novartis_de['P.Adj'].replace(0,1e-323).map(np.log10)*-1
+    # rename logFC column for concordance with Ginkgo columns
+    novartis_de.rename(columns={'LogFC':'logFC', 'P.Adj':'adj.P.Val'}, inplace=True)
+    return novartis_de
+
+def prepare_lincs_data(gene, URL='https://appyters.maayanlab.cloud/storage/DrugRegulators_Appyter/lincs_de'):
+    '''
+    gene: gene symbol to retrieve
+    URL: LINCS data storage location
+
+    output: results dataframe from LINCS data
+    '''
+    try:
+        lincs_de = pd.read_feather(f'{URL}/{gene}.f')
+    except:
+        # print(f'{gene} not found in LINCS')
+        return None
+     # format p-values
+    lincs_de['log10adj.P.Val'] = lincs_de['adj.P.Val'].replace(0,1e-323).map(np.log10)*-1
+    # remove CRISPR KO perturbations
+    lincs_ko_perturbs = pd.read_csv('https://appyters.maayanlab.cloud/storage/DrugRegulators_Appyter/lincs_ko_perturbs.txt', sep='\t')
+    lincs_de = lincs_de[~lincs_de['Drug'].isin(lincs_ko_perturbs.cmap_name.to_list())]
+    return lincs_de
+
+def hash_bucket(gene, num_buckets=512):
+    '''
+    gene: Gene symbol
+    num_buckets: number of hash buckets to create
+
+    output: integer hash for gene name (between 0-n_buckets)
+    '''
+    return int(hashlib.md5(gene.encode()).hexdigest(),16) % num_buckets
+
+def prepare_tahoe_data(df, gene):
+    '''
+    df: DataFrame retrieved from Tahoe gene bucket file
+    gene: gene to filter dataframe
+
+    output: results dataframe from Tahoe data filtered to gene
+    '''
+    tahoe_de = df[df['gene_name']==gene]
+    if tahoe_de.shape[0] == 0:
+        # print(f'{gene} not found in Tahoe')
+        return None
+    tahoe_de['log10adj.P.Val'] = tahoe_de['padj'].replace(0,1e-323).map(np.log10)*-1
+    tahoe_de.rename(columns = {'log2FoldChange':'logFC', 'drug':'Drug', 'padj':'adj.P.Val', 'group':'Perturbation', 'gene_name':'Gene'}, inplace=True)
+    tahoe_de['GeneDir'] = np.where(tahoe_de['UpReg']==1,'Up','Dn')
+    return tahoe_de
+
+def retrieve_tahoe_data(gene_set, URL='https://appyters.maayanlab.cloud/storage/DrugRegulators_Appyter/tahoe_de'):
+    '''
+    gene_set: list of gene symbols
+
+    output: dictionary of tahoe data for each gene in gene_set
+    '''
+    hash_dict = {}
+    for g in gene_set:
+        hash_dict[g] = str(hash_bucket(g))
+    hash_dict_rev = {}
+    for gene, hash in hash_dict.items():
+        if hash in hash_dict_rev:
+            hash_dict_rev[hash].append(gene)
+        else:
+            hash_dict_rev[hash]=[gene]
+    tahoe_results = {}
+    for hash, genes in hash_dict_rev.items():
+        df = pd.read_parquet(f'{URL}/gene_bucket_{hash}.parquet', use_pandas_metadata=False)
+        for g in genes:
+            tahoe_data = prepare_tahoe_data(df, g)
+            tahoe_results[g] = tahoe_data
+    return tahoe_results
+
+def prepare_ginkgo_data_dict(gene, cell_types, URL='https://appyters.maayanlab.cloud/storage/DrugRegulators_Appyter/ginkgo_de'):
+    '''
+    gene: gene symbol to retrieve
+    cell types: Ginkgo cell types in GDPx1 and GDPx2
+    URL: Ginkgo data storage location
+
+    output: dictionary with dataframes for all Ginkgo cell types
+    '''
+    try:
+        df = pd.read_feather(f'{URL}/{gene}.f')
+    except:
+        # print('Gene not found in Ginkgo')
+        return None
+    cell_type_results = {}
+    for k in cell_types:
+        subset = df[df['Perturbation'].str.contains(k)]
+        subset['log10adj.P.Val'] = subset['adj.P.Val'].replace(0,1e-323).map(np.log10)*-1
+        subset = subset.drop('index', axis=1)
+        cell_type_results[k] = subset
+
+    return cell_type_results
+
+def prepare_ginkgo_data_df(gene, cell_types, URL='https://appyters.maayanlab.cloud/storage/DrugRegulators_Appyter/ginkgo_de'):
+    '''
+    gene: gene symbol to retrieve
+    cell types: Ginkgo cell types in GDPx1 and GDPx2
+    URL: Ginkgo data storage location
+
+    output: results dataframe for all Ginkgo cell types
+    '''
+    try:
+
+        df = pd.read_feather(f'{URL}/{gene}.f')
+    except:
+        # print('Gene not found in Ginkgo')
+        return None
+    cell_type_results = {}
+    for k in cell_types:
+        subset = df[df['Perturbation'].str.contains(k)]
+        subset['log10adj.P.Val'] = subset['adj.P.Val'].replace(0,1e-323).map(np.log10)*-1
+        subset = subset.drop('index', axis=1)
+        cell_type_results[k] = subset
+
+    all_df = pd.concat(cell_type_results.values(), ignore_index=True)
+
+    return all_df