gdsfmt (version 1.8.3)

gdsfmt-package: R Interface to CoreArray Genomic Data Structure (GDS) files

Description

This package provides a high-level R interface to CoreArray Genomic Data Structure (GDS) data files, which are portable across platforms and include hierarchical structure to store multiple scalable array-oriented data sets with metadata information. It is suited for large-scale datasets, especially for data which are much larger than the available random-access memory. The gdsfmt package offers the efficient operations specifically designed for integers with less than 8 bits, since a single genetic/genomic variant, such like single-nucleotide polymorphism, usually occupies fewer bits than a byte. It is also allowed to read a GDS file in parallel with multiple R processes supported by the parallel package.

Details

Package:

gdsfmt

Type:

R/Bioconductor Package

License:

LGPL version 3

R interface of CoreArray GDS is based on the CoreArray project initiated and developed from 2007 (http://corearray.sourceforge.net). The CoreArray project is to develop portable, scalable, bioinformatic data visualization and storage technologies.

R is the most popular statistical environment, but one not necessarily optimized for high performance or parallel computing which ease the burden of large-scale calculations. To support efficient data management in parallel for numerical genomic data, we developed the Genomic Data Structure (GDS) file format. gdsfmt provides fundamental functions to support accessing data in parallel, and allows future R packages to call these functions.

Webpage: http://corearray.sourceforge.net, http://github.com/zhengxwen/gdsfmt

Copyright notice: The package includes the sources of CoreArray C++ library written by Xiuwen Zheng (LGPL-3), zlib written by Jean-loup Gailly and Mark Adler (zlib license), and LZ4 written by Yann Collet (simplified BSD).

References

http://corearray.sourceforge.net, http://github.com/zhengxwen/gdsfmt

Xiuwen Zheng, David Levine, Jess Shen, Stephanie M. Gogarten, Cathy Laurie, Bruce S. Weir. A High-performance Computing Toolset for Relatedness and Principal Component Analysis of SNP Data. Bioinformatics 2012; doi: 10.1093/bioinformatics/bts606.

Examples

# cteate a GDS file
f <- createfn.gds("test.gds")
L <- -2500:2499

# commom types
add.gdsn(f, "label", NULL)
add.gdsn(f, "int", val=1:10000, compress="ZIP", closezip=TRUE)
add.gdsn(f, "int.matrix", val=matrix(L, nrow=100, ncol=50))
add.gdsn(f, "mat", val=matrix(1:(10*6), nrow=10))
add.gdsn(f, "double", val=seq(1, 1000, 0.4))
add.gdsn(f, "character", val=c("int", "double", "logical", "factor"))
add.gdsn(f, "logical", val=rep(c(TRUE, FALSE, NA), 50))
add.gdsn(f, "factor", val=as.factor(c(letters, NA, "AA", "CC")))
add.gdsn(f, "NA", val=rep(NA, 10))
add.gdsn(f, "NaN", val=c(rep(NaN, 20), 1:20))
add.gdsn(f, "bit2-matrix", val=matrix(L[1:5000], nrow=50, ncol=100),
    storage="bit2")
# list and data.frame
add.gdsn(f, "list", val=list(X=1:10, Y=seq(1, 10, 0.25)))
add.gdsn(f, "data.frame", val=data.frame(X=1:19, Y=seq(1, 10, 0.5)))

# save a .RData object
obj <- list(X=1:10, Y=seq(1, 10, 0.1))
save(obj, file="tmp.RData")
addfile.gdsn(f, "tmp.RData", filename="tmp.RData")

f

read.gdsn(index.gdsn(f, "list"))
read.gdsn(index.gdsn(f, "list/Y"))
read.gdsn(index.gdsn(f, "data.frame"))
read.gdsn(index.gdsn(f, "mat"))

# Apply functions over columns of matrix
tmp <- apply.gdsn(index.gdsn(f, "mat"), margin=2, FUN=function(x) print(x))
tmp <- apply.gdsn(index.gdsn(f, "mat"), margin=2,
    selection = list(rep(c(TRUE, FALSE), 5), rep(c(TRUE, FALSE), 3)),
    FUN=function(x) print(x))

# close the GDS file
closefn.gds(f)


# delete the temporary file
unlink("test.gds", force=TRUE)