Function to use Enrichr's API

enrichr.py

def enrichr(dataframe, gene_set_libraries=None, kind="genes"):
    """
    Use Enrichr on a list of genes (currently only genes supported through the API).
    """
    import json
    import requests
    import pandas

    ENRICHR_ADD = 'http://amp.pharm.mssm.edu/Enrichr/addList'
    ENRICHR_RETRIEVE = 'http://amp.pharm.mssm.edu/Enrichr/enrich'
    query_string = '?userListId=%s&backgroundType=%s'

    if gene_set_libraries is None:
        gene_set_libraries = [
            'GO_Biological_Process_2015',
            "ChEA_2015",
            "KEGG_2016",
            "WikiPathways_2016",
            "Reactome_2016",
            "BioCarta_2016",
            "NCI-Nature_2016"
        ]

    results = pd.DataFrame()
    for gene_set_library in gene_set_libraries:
        print("Using enricher on %s gene set library." % gene_set_library)

        if kind == "genes":
            # Build payload with bed file
            attr = "\n".join(dataframe["gene_name"].dropna().tolist())
        elif kind == "regions":
            # Build payload with bed file
            attr = "\n".join(dataframe[['chrom', 'start', 'end']].apply(lambda x: "\t".join([str(i) for i in x]), axis=1).tolist())

        payload = {
            'list': (None, attr),
            'description': (None, gene_set_library)
        }
        # Request adding gene set
        response = requests.post(ENRICHR_ADD, files=payload)
        if not response.ok:
            raise Exception('Error adding gene list')

        # Track gene set ID
        user_list_id = json.loads(response.text)['userListId']

        # Request enriched sets in gene set
        response = requests.get(
            ENRICHR_RETRIEVE + query_string % (user_list_id, gene_set_library)
        )
        if not response.ok:
            raise Exception('Error fetching enrichment results')

        # Get enriched sets in gene set
        res = json.loads(response.text)
        # If there's no enrichemnt, continue
        if len(res) < 0:
            continue

        # Put in dataframe
        res = pd.DataFrame([pd.Series(s) for s in res[gene_set_library]])
        res.columns = ["rank", "description", "p_value", "z_score", "combined_score", "genes", "adjusted_p_value"]

        # Remember gene set library used
        res["gene_set_library"] = gene_set_library

        # Append to master dataframe
        results = results.append(res, ignore_index=True)

    return results