QTLtools-pca

Section: Bioinformatics tools (1)
Updated: 06 May 2020
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NAME

QTLtools pca - Conducts PCA  

SYNOPSIS

QTLtools pca --vcf [in.vcf|in.vcf.gz|in.bcf] | --bed in.bed.gz --out output.txt [OPTIONS]  

DESCRIPTION

This mode allows performing a Principal Component Analysis (PCA) either on molecular phenotype quantifications or genotype data. It is typically used (i) to detect outliers in the data, (ii) to detect stratification in the data or (iii) to build a covariate matrix before QTL mapping. QTLtools' PCA implementation utilizes singular value decomposition (SVD). When building a covariate matrix to account for technical covariates we recommend using --center and --scale.  

OPTIONS

--vcf [in.vcf|in.bcf|in.vcf.gz|in.bed.gz]

Genotypes in VCF/BCF/BED format. REQUIRED unless --bed.

--bed quantifications.bed.gz

Quantifications in BED format. REQUIRED unless --vcf.

--out output_prefix

Output file prefix. REQUIRED.

--center

Center the variables (genotypes or phenotypes) by subtracting the mean from each value

--scale

Scale the variables (genotypes or phenotypes) by dividing each value by the standard deviation

--region chr:start-end

Genomic region to be processed. E.g. chr4:12334456-16334456, or chr5

--exclude-chrs string

The chromosomes to exclude given as a space separated list. Only applies to --vcf. DEFAULT="X Y M MT XY chrX chrY chrM chrMT chrXY"

--maf float

Exclude sites with minor allele frequency less than this. Only applies to --vcf. DEFAULT=0.0

--distance integer

Only include sites separated with this many base pairs. Only applies to --vcf. DEFAULT=0

 

OUTPUT FILES

.pca

This file contains the principal components that were calculated. The names of the principal components, which is given in the first column, is composed of the output file prefix, whether the data was centered, whether the data was scaled, and the principal component number.

.pca_stats

This file contains the standard deviation of each principal component, and the variance and the cumulative variance explained by each PC.

 

EXAMPLES

o Running pca on RNAseq quantifications to calculate technical covariates:

QTLtools pca --bed genes.50percent.chr22.bed.gz --out genes.50percent.chr22 --center --scale

o Running pca on genotypes to detect population stratification:

QTLtools pca --vcf genotypes.chr22.vcf.gz --out genotypes.chr22 --center --scale --maf 0.05 --distance 5000

 

SEE ALSO

QTLtools(1)  

BUGS

o Versions up to and including 1.2, suffer from a bug in reading missing genotypes in VCF/BCF files. This bug affects variants with a DS field in their genotype's FORMAT and have a missing genotype (DS fiels is .) in one of the samples, in which case genotypes for all the samples are set to missing, effectively removing this variant from the analyses.

Please submit bugs to <https://github.com/qtltools/qtltools>  

AUTHORS

Halit Ongen (halitongen@gmail.com), Olivier Delaneau (olivier.delaneau@gmail.com)

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Index

NAME

SYNOPSIS

DESCRIPTION

OPTIONS

OUTPUT FILES

EXAMPLES

SEE ALSO

BUGS

AUTHORS

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Time: 14:11:25 GMT, August 24, 2020