download

download.py

from __future__ import print_function

import time

from xena_gdc_etl import xena_dataset, gdc


GDC_XENA_COHORT = [
    # 'TCGA-BRCA', #
    # 'TCGA-LUAD', #
    # 'TCGA-UCEC', #
    # 'TCGA-LGG', #
    # 'TCGA-HNSC', #
    # 'TCGA-PRAD', #
    # 'TCGA-LUSC', #
    # 'TCGA-THCA', #
    # 'TCGA-SKCM', #
    # 'TCGA-OV', #
    # 'TCGA-STAD', #
    # 'TCGA-COAD', #
    # 'TCGA-BLCA', #
    # 'TCGA-GBM', #
    # 'TCGA-LIHC', #
    # 'TCGA-KIRC', #
    # 'TCGA-CESC', #
    # 'TCGA-KIRP', #
    # 'TCGA-SARC', #
    # 'TCGA-ESCA', #
    # 'TCGA-PAAD', #
    # 'TCGA-PCPG', #
    # 'TCGA-READ', #
    # 'TCGA-TGCT', #
    'TCGA-LAML', #
    'TCGA-THYM', #
    'TCGA-ACC', #
    'TCGA-MESO', #
    'TCGA-UVM', #
    'TCGA-KICH', #
    'TCGA-UCS', #
    'TCGA-CHOL', #
    'TCGA-DLBC', #
]

COHORT_M27 = [
    "TCGA-OV",
    "TCGA-KIRC",
    "TCGA-BRCA",
    "TCGA-GBM",
    "TCGA-COAD",
    "TCGA-LUSC",
    "TCGA-LUAD",
    "TCGA-LAML",
    "TCGA-UCEC",
    "TCGA-READ",
    "TCGA-STAD",
    "TCGA-KIRP",
]

TARGET_DATA = [
    "TARGET-NBL", #
    "TARGET-AML", #
    "TARGET-WT", #
    "TARGET-OS", #
    "TARGET-ALL-P3", #
    "TARGET-RT", #
    "TARGET-CCSK", #
]

xena_dtypes = [
    'methylation27',  # Methylation Beta Value
    'masked_cnv',  # Masked Copy Number Segment 
    'mirna',  # miRNA Expression Quantification
    'muse_snv',  # MuSE Variant Aggregation and Masking
    'mutect2_snv',  # MuTect2 Variant Aggregation and Masking
    'somaticsniper_snv',  # SomaticSniper Variant Aggregation and Masking
    'varscan2_snv',  # VarScan2 Variant Aggregation and Masking
    'htseq_counts',  # HTSeq - Counts
    'htseq_fpkm',   # HTSeq - FPKM
    'htseq_fpkm-uq',  # HTSeq - FPKM-UQ
    'methylation450',  # Illumina Human Methylation 450 
]


# GDC_XENA_COHORT = ["TCGA-BRCA"]

for project in GDC_XENA_COHORT:
    for xena_dtype in xena_dtypes:
        if xena_dtype == "methylation27" and project not in COHORT_M27:
            continue
        dataset = xena_dataset.GDCOmicset(
            projects=project,
            root_dir=r"./genomic-data",
            xena_dtype=xena_dtype,
        )
        dataset.download().transform().metadata()