I am trying to run MutSIgCV and got stuck with this error:

```
MutSigCV allsamples.md.tc.ir.br.pr.ug.dbsnp.vep.maf \
"$anno"exome_full192.coverage.txt \
"$anno"gene.covariates.txt \
my_results \
"$anno"mutation_type_dictionary_file.txt \
"$anno"chr_files_hg19

======================================
MutSigCV
v1.4

(c) Mike Lawrence and Gaddy Getz
Broad Institute of MIT and Harvard
======================================

MutSigCV: PREPROCESS
--------------------
Loading mutation_file...
Error using MutSigCV>MutSig_preprocess (line 246)
MutSig is not applicable to single patients.\n

Error in MutSigCV (line 184)
```

I suspect this is because I did not create the maf file properly. A run-down of the experiment:

 - variants were called using the GATK pipeline (unified genotyper)
 - annotated with VEP
 - vcf was converted to maf using the tool [vcf2maf][1]

 [1]: https://github.com/ckandoth/vcf2maf

I am not working with cancer data.

Questions:

 - is the issue due to the maf?
 - If yes, how can it be prepared to include patient information?

A few of the vcf (header removed, this is a test so only chr14 is present):

```
##INFO=
#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT Psio10_gDNA Psio11_gDNA Psio12_gDNA Psio1_gDNA Psio2_gDNA Psio3_gDNA Psio4_gDNA Psio5_gDNA Psio6_gDNA Psio7_gDNA Psio8_gDNA Psio9_gDNA
chr14 62290315 . G A 27.75 LowQual AC=4;AF=0.250;AN=16;BaseCounts=4,0,9,0;BaseQRankSum=-1.271;DP=13;Dels=0.00;FS=0.000;HaplotypeScore=0.0000;MLEAC=3;MLEAF=0.188;MQ=50.39;MQ0=0;MQRankSum=0.480;QD=13.88;ReadPosRankSum=-1.733;CSQ=intron_variant&nc_transcript_variant|||ENSG00000258882|CTD-2277K2.1|ENST00000554138|||||lincRNA GT:AD:DP:GQ:PL 0/0:2,0:2:6:0,6,69 0/0:2,0:2:6:0,6,68 ./. ./. ./. ./. 1/1:0,1:1:3:32,3,0 0/0:1,0:1:3:0,3,32 1/1:0,1:1:3:33,3,0 0/0:1,1:2:3:0,3,33 0/0:2,0:2:3:0,3,33 0/0:1,0:1:3:0,3,33
chr14 62292953 . A T 10.92 LowQual AC=2;AF=1.00;AN=2;BaseCounts=0,0,0,1;DP=1;Dels=0.00;FS=0.000;HaplotypeScore=0.0000;MLEAC=2;MLEAF=1.00;MQ=60.00;MQ0=0;QD=10.92;CSQ=intron_variant&nc_transcript_variant|||ENSG00000258882|CTD-2277K2.1|ENST00000554138|||||lincRNA GT:AD:DP:GQ:PL ./. ./. 1/1:0,1:1:3:33,3,0 ./. ./. ./. ./. ./. ./. ./. ./. ./.
chr14 62293825 . G T 20.64 LowQual AC=2;AF=0.167;AN=12;BaseCounts=0,0,9,2;BaseQRankSum=-1.296;DP=11;Dels=0.00;FS=0.000;HaplotypeScore=0.1662;MLEAC=2;MLEAF=0.167;MQ=60.00;MQ0=0;MQRankSum=0.354;QD=4.13;ReadPosRankSum=0.354;CSQ=downstream_gene_variant|||ENSG00000258956|COX4I1P1|ENST00000554239|||||processed_pseudogene,intron_variant&nc_transcript_variant|||ENSG00000258882|CTD-2277K2.1|ENST00000554138|||||lincRNA GT:AB:AD:DP:GQ:PL ./. 0/0:.:2,0:2:6:0,6,68 ./. ./. ./. ./. 0/0:.:1,0:1:3:0,3,33 0/1:0.670:2,1:3:26:26,0,39 ./. 0/0:.:2,0:2:6:0,6,68 0/1:0.500:1,1:2:27:27,0,27 0/0:.:1,0:1:3:0,3,33
chr14 62294744 . G A 28.05 LowQual AC=3;AF=0.300;AN=10;BaseCounts=2,1,5,0;BaseQRankSum=-0.198;DP=8;Dels=0.00;FS=0.000;HaplotypeScore=0.0000;MLEAC=3;MLEAF=0.300;MQ=57.05;MQ0=0;MQRankSum=0.198;QD=9.35;ReadPosRankSum=0.198;CSQ=downstream_gene_variant|||ENSG00000258956|COX4I1P1|ENST00000554239|||||processed_pseudogene,intron_variant&nc_transcript_variant|||ENSG00000258882|CTD-2277K2.1|ENST00000554138|||||lincRNA GT:AB:AD:DP:GQ:PL ./. ./. 1/1:.:0,1:1:3:33,3,0 ./. 0/0:.:1,0:1:3:0,3,33 0/1:0.500:1,1:2:26:26,0,26 ./. 0/0:.:1,0:1:3:0,3,32 0/0:.:2,0:2:3:0,3,45 ./. ./. ./.
chr14 62299005 . A C 144.99 . AC=1;AF=0.042;AN=24;BaseCounts=301,8,0,0;BaseQRankSum=-4.723;DP=309;Dels=0.00;FS=2.199;HaplotypeScore=0.9147;InbreedingCoeff=-0.0435;MLEAC=1;MLEAF=0.042;MQ=59.70;MQ0=0;MQRankSum=0.508;QD=7.63;ReadPosRankSum=-1.488;CSQ=non_coding_exon_variant&nc_transcript_variant|||ENSG00000258956|COX4I1P1|ENST00000554239|1/1||||processed_pseudogene,intron_variant&nc_transcript_variant|||ENSG00000258882|CTD-2277K2.1|ENST00000554138|||||lincRNA GT:AB:AD:DP:GQ:PL 0/0:.:26,0:26:78:0,78,892 0/0:.:29,0:29:81:0,81,927 0/0:.:17,0:17:45:0,45,528 0/0:.:28,0:28:75:0,75,861 0/0:.:25,0:25:69:0,69,777 0/0:.:35,0:35:99:0,102,1127 0/0:.:38,0:38:99:0,102,1149 0/1:0.580:11,8:19:99:180,0,294 0/0:.:24,0:24:66:0,66,764 0/0:.:33,0:33:93:0,93,1065 0/0:.:16,0:16:45:0,45,502 0/0:.:19,0:19:54:0,54,603
```
<a href="https://github.com/mskcc/vcf2maf">vcf2maf</a> doesn't support multi-sample VCFs, like the sample you've shown. When `--tumor-id` is not specified, the resulting MAF will name all the samples as `TUMOR`... which is why MutSig thinks you have a single patient. Even though you're not working with cancer, it is perfectly fine to set `--tumor-id` to your sample ID, as long as it matches the corresponding column header in the VCF e.g. `Psio10_gDNA`. But you will first need to split your VCF into per-sample VCFs, run vcf2maf on each, concatenate the resulting MAFs, and feed that into MutSig. Here is a bit of code to help out:

    # Download and unpack VCFtools. You don't need to compile it, because you'll only need the Perl utils for now:
    wget http://downloads.sourceforge.net/project/vcftools/vcftools_0.1.12b.tar.gz
    tar -zxf vcftools_0.1.12b.tar.gz

    # Use VCFtools' vcf-query to make a list of all the sample IDs in the multisample VCF:
    perl -I vcftools_0.1.12b/perl vcftools_0.1.12b/perl/vcf-query --list-columns allsamples.vcf > sample_ids

    # For each sample ID, run vcf-subset to create per-sample VCFs in a subfolder:
    mkdir vcf2maf
    cat sample_ids | perl -ne 'chomp; print `cat allsamples.vcf | perl -I vcftools_0.1.12b/perl vcftools_0.1.12b/perl/vcf-subset --exclude-ref --columns $_ > vcf2maf/$_.vcf`'

    # For each VCF, run vcf2maf with the `--tumor-id` specified, to create per-sample MAFs into the subfolder:
    cat sample_ids | perl -ne 'chomp; print `perl vcf2maf.pl --input-vcf vcf2maf/$_.vcf --output-maf vcf2maf/$_.vep.maf --tumor-id $_`'

    # Concatenate the per-sample MAFs together, making sure that the MAF header is not duplicated:
    cat vcf2maf/*.maf | egrep "^#|^Hugo_Symbol" | head -2 > allsamples.vep.maf
    cat vcf2maf/*.maf | egrep -v "^#|^Hugo_Symbol" >> allsamples.vep.maf

In addition to MutSig, you can also try the SMG test from the <a href="http://tvap.genome.wustl.edu/tools/music/">MuSiC suite</a> of tools developed at my former lab. It is not cancer specific, and can also find significantly altered regulatory regions, based on how you define your regions of interest.