combine_tracks.wdl should do the pre-processing for input to GISTIC2

[LeeTL1220]

Feature request

Tool(s) or class(es) involved

combine_tracks.wdl

Description

In order for outputs from GATK CNV to be usable by GISTIC2, we need to have a conversion step. Here is mostly un-tested WDL that should work:

#UNSUPPORTED -- simple conversion of a merged & pruned seg file (from the CNV postprocessing workflow) to the GISTIC2 format.
# No column headers printed.  Each column is:
#
#(1)  Sample           (sample name)
#(2)  Chromosome  (chromosome number)
#(3)  Start Position  (segment start position, in bases)
#(4)  End Position   (segment end position, in bases)
#(5)  Num markers      (number of markers in segment)
#(6)  Seg.CN       (log2() -1 of copy number)
#
# This has barely been tested
#
workflow ConvertMergedPrunedSegsToGistic2 {
    File cnv_postprocessing_tumor_with_tracks_pruned_merged_seg
    String docker
    call Gistic2Convert {
        input:
            input_file = cnv_postprocessing_tumor_with_tracks_pruned_merged_seg,
            docker = docker
    }

    output {
        File cnv_postprocessing_tumor_with_tracks_pruned_merged_seg_gistic2 = Gistic2Convert.output_file_gistic2
    }
}

task Gistic2Convert {
    File input_file
    String docker
    String output_file = basename(input_file) + ".gistic2.seg"

    command <<<
        set -e
        python <<EOF
import csv
input_file = "${input_file}"
output_file = "${output_file}"

"""
  The column headers are:

(1)  Sample           (sample name)
(2)  Chromosome  (chromosome number)
(3)  Start Position  (segment start position, in bases)
(4)  End Position   (segment end position, in bases)
(5)  Num markers      (number of markers in segment)
(6)  Seg.CN       (log2() -1 of copy number)
"""

if __name__ == "__main__":
    with open(input_file, 'rb') as tsvinfp, open(output_file, 'wb') as tsvoutfp:
        tsvin = csv.DictReader(tsvinfp, delimiter='\t')
        tsvout = csv.writer(tsvoutfp, delimiter="\t")
        for r in tsvin:
            int_ify_num_points = r["NUM_POINTS_COPY_RATIO"].replace(".0", "")
            outrow = [r["SAMPLE"], r["Chromosome"], r["Start"], r["End"], int_ify_num_points, r["MEAN_LOG2_COPY_RATIO"]]
            print(outrow)
            tsvout.writerow(outrow)

EOF
    >>>

    runtime {
        docker: docker
        memory: "2000 MB"
        disks: "local-disk 100 HDD"
        preemptible: 3
        cpu: 1
    }
    output {
        File output_file_gistic2 = "${output_file}"
    }
}

Sorry about the heredoc python