Assess the prevalence of the various genes in the MHC pathway and return a report in the tsv
    format.

    :param toil.fileStore.FileID gene_expression: fsID for the rsem gene expression file
    :param toil.fileStore.FileID|None rna_haplotype: fsID for the RNA PHLAT file
    :param dict univ_options: Dict of universal options used by almost all tools
    :param dict reports_options: Options specific to reporting modules
    :return: The fsID for the mhc pathway report file
    :rtype: toil.fileStore.FileID

def assess_mhc_genes(job, gene_expression, rna_haplotype, univ_options, reports_options):
    """
    Assess the prevalence of the various genes in the MHC pathway and return a report in the tsv
    format.

    :param toil.fileStore.FileID gene_expression: fsID for the rsem gene expression file
    :param toil.fileStore.FileID|None rna_haplotype: fsID for the RNA PHLAT file
    :param dict univ_options: Dict of universal options used by almost all tools
    :param dict reports_options: Options specific to reporting modules
    :return: The fsID for the mhc pathway report file
    :rtype: toil.fileStore.FileID
    """

    work_dir = os.getcwd()
    # Take file parameters for both TCGA and GTEX files
    tumor_type = univ_options['tumor_type']
    b_types = {
    'tcga': tumor_type + " normal",
    'gtex': TCGAToGTEx[tumor_type] if tumor_type in TCGAToGTEx else "NA"}

    input_files = {
        'rsem_quant.tsv': gene_expression,
        'mhc_pathways.tsv.tar.gz': reports_options['mhc_pathways_file']}
    if rna_haplotype is not None:
        input_files['rna_haplotype.sum'] = rna_haplotype
    input_files = get_files_from_filestore(job, input_files, work_dir, docker=False)

    input_files['mhc_pathways.tsv'] = untargz(input_files['mhc_pathways.tsv.tar.gz'], work_dir)

    # Read the background file

    background_df = pd.read_table(input_files['mhc_pathways.tsv'], index_col=0, header=0)

    # Parse the rna phlat file
    if rna_haplotype is not None:
        with open(input_files['rna_haplotype.sum']) as rna_mhc:
            mhc_alleles = {'HLA_A': [], 'HLA_B': [], 'HLA_C': [], 'HLA_DPA': [], 'HLA_DQA': [],
                           'HLA_DPB': [], 'HLA_DQB': [], 'HLA_DRB': []}
            mhc_alleles = parse_phlat_file(rna_mhc, mhc_alleles)

    # Read the patient gene values into a dictionary
    gene_expressions = pd.read_table(input_files['rsem_quant.tsv'], index_col=0, header=0)
    gene_expressions = Counter({x.split('.')[0]: y for x, y in gene_expressions['TPM'].to_dict().items()})
    # Print the report
    roles = {x for x in background_df['Roles'].values if ',' not in x}
    with open('mhc_pathway_report.txt', 'w') as mpr:
        for role in roles:
            role_df = background_df[background_df['Roles'].str.contains(role)]
            print(role.center(90, ' '), file=mpr)
            print(
                "{:12}{:<12}{:<17}{:<12}{:<20}{:<17}\n".format('Gene', 'Observed', 'Threshold_GTEX',
                                                                 'Result', 'Threshold_TCGA_N', 'Result'),
                file=mpr)
            # If tumor_type in TCGAToGTEx.keys():
            if role == 'MHCI loading':
                for mhci_allele in 'HLA_A', 'HLA_B', 'HLA_C':
                    if rna_haplotype is not None:
                        num_alleles = len(mhc_alleles[mhci_allele])
                        result = ('FAIL' if num_alleles == 0 else
                                  'LOW' if num_alleles == 1 else
                                  'PASS')
                    else:
                        result = num_alleles = 'NA'
                    print("{:12}{:<12}{:<17}{:<12}{:<20}{:<17}".format(mhci_allele, 2,
                                                                           num_alleles, result,
                                                                           2, result), file=mpr)
            elif role == 'MHCII loading':
                for mhcii_allele in ('HLA_DQA', 'HLA_DQB', 'HLA_DRB'):
                    if rna_haplotype is not None:
                        num_alleles = len(mhc_alleles[mhcii_allele])
                        result = ('FAIL' if num_alleles == 0 else
                                  'LOW' if num_alleles == 1 else
                                  'PASS')
                    else:
                        result = num_alleles = 'NA'
                    print(
                        "{:12}{:<12}{:<17}{:<12}{:<20}{:<17}".format(mhcii_allele, 2, num_alleles,
                                                                     result, 2, result), file=mpr)

            for ensg in role_df.index:
                ensgName = background_df.ix[ensg, 'Name']
                b_vals = {}
                for bkg in b_types:
                    val = "{0:.2f}".format(role_df.loc[ensg].get(b_types[bkg], default='NA'))
                    result = ('NA' if val == 'NA' else
                              'LOW' if float(val) >= float(gene_expressions[ensg]) else
                              'PASS')
                    b_vals[bkg] = val, result
                print(
                    "{:12}{:<12}{:<17}{:<12}{:<20}{:<17}".format(ensgName, float(gene_expressions[ensg]),
                                                                 b_vals['gtex'][0], b_vals['gtex'][1],
                                                                 b_vals['tcga'][0], b_vals['tcga'][1]),
                    file=mpr)

            print('\n', file=mpr)

    output_file = job.fileStore.writeGlobalFile(mpr.name)
    export_results(job, output_file, mpr.name, univ_options, subfolder='reports')
    job.fileStore.logToMaster('Ran mhc gene assessment on %s successfully'
                              % univ_options['patient'])
    return output_file