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gmt music clinical-correlation


       gmt music clinical-correlation - Correlate phenotypic traits against mutated genes, or
       against individual variants


       This document describes gmt music clinical-correlation version 0.04 (2018-07-05 at


       gmt music clinical-correlation --bam-list=? --output-file=? [--maf-file=?]
       [--glm-clinical-data-file=?] [--use-maf-in-glm] [--skip-non-coding] [--skip-silent]
       [--clinical-correlation-matrix-file=?] [--input-clinical-correlation-matrix-file=?]
       [--genetic-data-type=?] [--numeric-clinical-data-file=?] [--numerical-data-test-method=?]
       [--categorical-clinical-data-file=?] [--glm-model-file=?]

        ... music clinical-correlation \
               --bam-list /path/myBamList.tsv \
               --maf-file /path/myMAF.tsv \
               --numeric-clinical-data-file /path/myNumericData.tsv \
               --genetic-data-type 'gene' \
               --output-file /path/output_file

        ... music clinical-correlation \
               --maf-file /path/myMAF.tsv \
               --bam-list /path/myBamList.tsv \
               --numeric-clinical-data-file /path/myNumericData.tsv \
               --categorical-clinical-data-file /path/myClassData.tsv \
               --genetic-data-type 'gene' \
               --output-file /path/output_file

        ... music clinical-correlation \
               --maf-file /path/myMAF.tsv \
               --bam-list /path/myBamList.tsv \
               --output-file /path/output_file \
               --glm-model-file /path/model.tsv \
               --glm-clinical-data-file /path/glm_clinical_data.tsv \


       bam-list  Text
           Tab delimited list of BAM files [sample_name, normal_bam, tumor_bam] (See Description)

       output-file  Text
           Results of clinical-correlation tool. Will have suffix added for data type


       maf-file  Text
           List of mutations using TCGA MAF specification v2.3

       glm-clinical-data-file  Text
           Clinical traits, mutational profiles, other mixed clinical data (See DESCRIPTION)

       use-maf-in-glm  Boolean
           Create a variant matrix from the MAF file as variant input to GLM analysis.

           Default value 'false' (--nouse-maf-in-glm) if not specified

       nouse-maf-in-glm  Boolean
           Make use-maf-in-glm 'false'

       skip-non-coding  Boolean
           Skip non-coding mutations from the provided MAF file

           Default value 'true' if not specified

       noskip-non-coding  Boolean
           Make skip-non-coding 'false'

       skip-silent  Boolean
           Skip silent mutations from the provided MAF file

           Default value 'true' if not specified

       noskip-silent  Boolean
           Make skip-silent 'false'

       clinical-correlation-matrix-file  Text
           Specify a file to store the sample-vs-gene matrix created during calculations

       input-clinical-correlation-matrix-file  Text
           Instead of creating this from the MAF, input the sample-vs-gene matrix for

       genetic-data-type  Text
           Correlate clinical data to "gene" or "variant" level data

           Default value 'gene' if not specified

       numeric-clinical-data-file  Text
           Table of samples (y) vs. numeric clinical data category (x)

       numerical-data-test-method  Text
           Either 'cor' for Pearson Correlation or 'wilcox' for the Wilcoxon Rank-Sum Test for
           numerical clinical data

           Default value 'cor' if not specified

       categorical-clinical-data-file  Text
           Table of samples (y) vs. categorical clinical data category (x)

       glm-model-file  Text
           File outlining the type of model, response variable, covariants, etc. for the GLM
           analysis. (See DESCRIPTION)


       This command relates clinical traits and mutational data. Either one can perform
       correlation analysis between mutations recorded in a MAF and the particular phenotypic
       traits recorded in clinical data files for the same samples, or one can run a generalized
       linear model (GLM) analysis on the same types of data.

       The clinical data files for correlation must be separated between numeric and categoric
       data and must follow these conventions:

       ·   Headers are required

       ·   Each file must include at least 1 sample_id column and 1 attribute column, with the
           format being [sample_id  clinical_data_attribute_1 clinical_data_attribute_2 ...]

       ·   The sample ID must match the sample ID listed in the MAF under "Tumor_Sample_Barcode"
           for relating the mutations of this sample.

       Note the importance of the headers: the header for each clinical_data_attribute will
       appear in the output file to denote relationships with the mutation data from the MAF.

       Internally, the input data is fed into an R script which calculates a P-value representing
       the probability that the correlation seen between the mutations in each gene (or variant)
       and each phenotype trait are random. Lower P-values indicate lower randomness, or likely
       true correlations.

       The results are saved to the output filename given with a suffix appended; ".numeric.csv"
       will be appended for results derived from numeric clinical data, and ".categorical.csv"
       will be appended for results derived from categorical clinical data. Also, ".glm.csv" will
       be appended to the output filename for GLM results.

       The GLM analysis accepts a mixed numeric and categoric clinical data file, input using the
       parameter --glm-clinical-data-file. GLM clinical data must adhere to the formats described
       above for the correlation clinical data files. GLM also requires the user to input a
       --glm-model-file. This file requires specific headers and defines the analysis to be
       performed rather exactly. Here are the conventions required for this file:

       ·   Columns must be ordered as such:

       ·   [ analysis_type    clinical_data_trait_name    variant/gene_name   covariates  memo ]

       ·   The 'analysis_type' column must contain either "Q", indicating a quantative trait, or
           "B", indicating a binary trait will be examined.

       ·   The 'clinical_data_trait_name' is the name of a clinical data trait defined by being a
           header in the --glm-clinical-data-file.

       ·   The 'variant/gene_name' can either be the name of one or more columns from the
           --glm-clinical-data-file, or the name of one or more mutated gene names from the MAF,
           separated by "|". If this column is left blank, or instead contains "NA", then each
           column from either the variant mutation matrix (--use-maf-in-glm) or alternatively the
           --glm-clinical-data-file is used consecutively as the variant column in independent

       ·   'covariates' are the names of one or more columns from the --glm-clinical-data-file,
           separated by "+".

       ·   'memo' is any note deemed useful to the user. It will be printed in the output data
           file for reference.

       GLM analysis may be performed using solely the data input into --glm-clinical-data-file,
       as described above, or alternatively, mutational data from the MAF may be included as
       variants in the GLM analysis, as also described above. Use the --use-maf-in-glm flag to
       include the mutation matrix derived from the maf as variant data.

       Note that all input files for both correlation and GLM analysis must be tab-separated.


           Provide a file containing sample names and normal/tumor BAM locations for each. Use
           the tab- delimited format [sample_name normal_bam tumor_bam] per line. This tool only
           needs sample_name, so all other columns can be skipped. The sample_name must be the
           same as the tumor sample names used in the MAF file (16th column, with the header


       Copyright (C) 2010-2011 Washington University in St. Louis.

       It is released under the Lesser GNU Public License (LGPL) version 3.  See the associated
       LICENSE file in this distribution.


        Nathan D. Dees, Ph.D.
        Qunyuan Zhang, Ph.D.
        William Schierding, M.S.


       genome-music(1), genome(1)