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Last data update: 2014.03.03

R: Calculate WAD statistic for individual genes

WAD	R Documentation

Calculate WAD statistic for individual genes

Description

This function performs WAD method to identify differentially expressed genes (DEGs) from two-group gene expression data. A high absolute value for the WAD statistic is evident of a high degree of differential expression.

Usage

WAD(data, group, logged = FALSE, floor = 1, sort = FALSE)

Arguments

`data`	numeric matrix or data frame containing count data or microarray data, where each row indicates the gene (or transcript or probeset ID), each column indicates the sample (or library), and each cell indicates the expression value (i.e., number of counts or signal intensity) of the gene in the sample.
`group`	numeric vector indicating the experimental group for each sample (or library).
`logged`	logical. If `TRUE`, the input data are regarded as log2-transformed. If `FALSE`, the log2-transformation is performed after the floor setting. The default is `logged = FALSE`.
`floor`	numeric scalar (> 0) specifying the floor value for taking logarithm. The default is `floor = 1`, indicating that values less than 1 are replaced by 1. Ignored if `logged = TRUE`.
`sort`	logical. If `TRUE`, the retrieved results are sorted in order of the rank of absolute WAD statistic. If `FALSE`, the results are retrieved by the original order.

Value

A numeric vector of WAD statistic for individual genes

References

Kadota K, Nakai Y, Shimizu K: A weighted average difference method for detecting differentially expressed genes from microarray data. Algorithms Mol Biol. 2008, 3: 8.

Examples

data(nakai)
group <- c(1, 1, 1, 1, 2, 2, 2, 2)

wad <- WAD(nakai, group, logged = TRUE, sort = TRUE)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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Platform: x86_64-pc-linux-gnu (64-bit)

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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(TCC)
Loading required package: DESeq
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

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    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Loading required package: locfit
locfit 1.5-9.1 	 2013-03-22
Loading required package: lattice
    Welcome to 'DESeq'. For improved performance, usability and
    functionality, please consider migrating to 'DESeq2'.
Loading required package: DESeq2
Loading required package: S4Vectors
Loading required package: stats4

Attaching package: 'S4Vectors'

The following objects are masked from 'package:base':

    colMeans, colSums, expand.grid, rowMeans, rowSums

Loading required package: IRanges
Loading required package: GenomicRanges
Loading required package: GenomeInfoDb
Loading required package: SummarizedExperiment

Attaching package: 'DESeq2'

The following objects are masked from 'package:DESeq':

    estimateSizeFactorsForMatrix, getVarianceStabilizedData,
    varianceStabilizingTransformation

Loading required package: edgeR
Loading required package: limma

Attaching package: 'limma'

The following object is masked from 'package:DESeq2':

    plotMA

The following object is masked from 'package:DESeq':

    plotMA

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Loading required package: baySeq
Loading required package: abind
Loading required package: perm
Loading required package: ROC

Attaching package: 'TCC'

The following object is masked from 'package:edgeR':

    calcNormFactors

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/TCC/WAD.Rd_%03d_medium.png", width=480, height=480)
> ### Name: WAD
> ### Title: Calculate WAD statistic for individual genes
> ### Aliases: WAD
> 
> ### ** Examples
> 
> data(nakai)
> group <- c(1, 1, 1, 1, 2, 2, 2, 2)
> 
> wad <- WAD(nakai, group, logged = TRUE, sort = TRUE)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>