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Last data update: 2014.03.03

R: Create a SNP genotype dataset from a matrix

snpgdsCreateGeno

R Documentation

Create a SNP genotype dataset from a matrix

Description

To create a GDS file of genotypes from a matrix.

Usage

snpgdsCreateGeno(gds.fn, genmat, sample.id=NULL, snp.id=NULL, snp.rs.id=NULL,
    snp.chromosome=NULL, snp.position=NULL, snp.allele=NULL, snpfirstdim=TRUE,
    compress.annotation="ZIP_RA.max", compress.geno="", other.vars=NULL)

Arguments

`gds.fn`	the file name of gds
`genmat`	a matrix of genotypes
`sample.id`	the sample ids, which should be unique
`snp.id`	the SNP ids, which should be unique
`snp.rs.id`	the rs ids for SNPs, which can be not unique
`snp.chromosome`	the chromosome indices
`snp.position`	the SNP positions in basepair
`snp.allele`	the reference/non-reference alleles
`snpfirstdim`	if TRUE, genotypes are stored in the individual-major mode, (i.e, list all SNPs for the first individual, and then list all SNPs for the second individual, etc)
`compress.annotation`	the compression method for the variables except `genotype`
`compress.geno`	the compression method for the variable `genotype`
`other.vars`	a list object storing other variables

Details

There are possible values stored in the variable genmat: 0, 1, 2 and other values. “0” indicates two B alleles, “1” indicates one A allele and one B allele, “2” indicates two A alleles, and other values indicate a missing genotype.

If snpfirstdim is TRUE, then genmat should be “# of SNPs X # of samples”; if snpfirstdim is FALSE, then genmat should be “# of samples X # of SNPs”.

The typical variables specified in other.vars are “sample.annot” and “snp.annot”, which are data.frame objects.

Value

None.

Author(s)

Xiuwen Zheng

Examples

# load data
data(hapmap_geno)

# create a gds file
with(hapmap_geno, snpgdsCreateGeno("test.gds", genmat=genotype,
    sample.id=sample.id, snp.id=snp.id, snp.chromosome=snp.chromosome,
    snp.position=snp.position, snp.allele=snp.allele, snpfirstdim=TRUE))

# open the gds file
genofile <- snpgdsOpen("test.gds")

RV <- snpgdsPCA(genofile)
plot(RV$eigenvect[,2], RV$eigenvect[,1], xlab="PC 2", ylab="PC 1")

# close the file
snpgdsClose(genofile)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> library(SNPRelate)
Loading required package: gdsfmt
SNPRelate -- supported by Streaming SIMD Extensions 2 (SSE2)
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/SNPRelate/snpgdsCreateGeno.Rd_%03d_medium.png", width=480, height=480)
> ### Name: snpgdsCreateGeno
> ### Title: Create a SNP genotype dataset from a matrix
> ### Aliases: snpgdsCreateGeno
> ### Keywords: GDS GWAS
> 
> ### ** Examples
> 
> # load data
> data(hapmap_geno)
> 
> # create a gds file
> with(hapmap_geno, snpgdsCreateGeno("test.gds", genmat=genotype,
+     sample.id=sample.id, snp.id=snp.id, snp.chromosome=snp.chromosome,
+     snp.position=snp.position, snp.allele=snp.allele, snpfirstdim=TRUE))
> 
> # open the gds file
> genofile <- snpgdsOpen("test.gds")
> 
> RV <- snpgdsPCA(genofile)
Principal Component Analysis (PCA) on SNP genotypes:
Excluding 42 SNPs on non-autosomes
Excluding 0 SNP (monomorphic: TRUE, < MAF: NaN, or > missing rate: NaN)
Working space: 279 samples, 958 SNPs
	using 1 (CPU) core
PCA:	the sum of all selected genotypes (0, 1 and 2) = 264760
Wed Jul  6 05:34:32 2016    (internal increment: 1744)
 [>.................................................]  0%, ETC: NA     [==================================================] 100%, completed  
Wed Jul  6 05:34:32 2016    Begin (eigenvalues and eigenvectors)
Wed Jul  6 05:34:32 2016    Done.
> plot(RV$eigenvect[,2], RV$eigenvect[,1], xlab="PC 2", ylab="PC 1")
> 
> # close the file
> snpgdsClose(genofile)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>