Deviance and Pearson Residuals for the Negative Binomial Model of edgeR

Description

This function implements the residuals method for the edgeR function glmFit.

Usage

## S3 method for class 'DGEGLM'
residuals(object, type = c("deviance", "pearson"), ...)

Arguments

Value

A genes-by-samples numeric matrix with the negative binomial residuals for each gene and sample.

Author(s)

Davide Risso

References

McCullagh P, Nelder J (1989). Generalized Linear Models. Chapman and Hall, New York.

Venables, W. N. and Ripley, B. D. (1999). Modern Applied Statistics with S-PLUS. Third Edition. Springer.

Examples

library(edgeR)
library(zebrafishRNASeq)
data(zfGenes)

## run on a subset genes for time reasons 
## (real analyses should be performed on all genes)
genes <- rownames(zfGenes)[grep("^ENS", rownames(zfGenes))]
spikes <- rownames(zfGenes)[grep("^ERCC", rownames(zfGenes))]
set.seed(123)
idx <- c(sample(genes, 1000), spikes)
seq <- newSeqExpressionSet(as.matrix(zfGenes[idx,]))

x <- as.factor(rep(c("Ctl", "Trt"), each=3))
design <- model.matrix(~x)
y <- DGEList(counts=counts(seq), group=x)
y <- calcNormFactors(y, method="upperquartile")
y <- estimateGLMCommonDisp(y, design)
y <- estimateGLMTagwiseDisp(y, design)

fit <- glmFit(y, design)
res <- residuals(fit, type="deviance")
head(res)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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> library(RUVSeq)
Loading required package: Biobase
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

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

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    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

Welcome to Bioconductor

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

Loading required package: EDASeq
Loading required package: ShortRead
Loading required package: BiocParallel
Loading required package: Biostrings
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: XVector
Loading required package: Rsamtools
Loading required package: GenomeInfoDb
Loading required package: GenomicRanges
Loading required package: GenomicAlignments
Loading required package: SummarizedExperiment
Loading required package: edgeR
Loading required package: limma

Attaching package: 'limma'

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

    plotMA

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/RUVSeq/residuals.DGEGLM.Rd_%03d_medium.png", width=480, height=480)
> ### Name: residuals.DGEGLM
> ### Title: Deviance and Pearson Residuals for the Negative Binomial Model
> ###   of 'edgeR'
> ### Aliases: residuals.DGEGLM
> 
> ### ** Examples
> 
> library(edgeR)
> library(zebrafishRNASeq)
> data(zfGenes)
> 
> ## run on a subset genes for time reasons 
> ## (real analyses should be performed on all genes)
> genes <- rownames(zfGenes)[grep("^ENS", rownames(zfGenes))]
> spikes <- rownames(zfGenes)[grep("^ERCC", rownames(zfGenes))]
> set.seed(123)
> idx <- c(sample(genes, 1000), spikes)
> seq <- newSeqExpressionSet(as.matrix(zfGenes[idx,]))
> 
> x <- as.factor(rep(c("Ctl", "Trt"), each=3))
> design <- model.matrix(~x)
> y <- DGEList(counts=counts(seq), group=x)
> y <- calcNormFactors(y, method="upperquartile")
> y <- estimateGLMCommonDisp(y, design)
> y <- estimateGLMTagwiseDisp(y, design)
> 
> fit <- glmFit(y, design)
> res <- residuals(fit, type="deviance")
> head(res)
                            [,1]          [,2]          [,3]          [,4]
ENSDARG00000043686 -0.4451085505  0.6419776552 -0.5013664759 -0.0001414214
ENSDARG00000089089 -0.0001414214 -0.0001414214 -0.0001414214 -0.0001414214
ENSDARG00000060813 -0.0306521830 -0.2307841132  0.2297720295 -0.4940380472
ENSDARG00000092245 -1.4574454937  0.6847358009 -0.1636637476 -1.7267788266
ENSDARG00000094339 -0.0001414214 -0.0001414214 -0.0001414214 -0.0001414214
ENSDARG00000007918  0.7764421556 -0.5915569355 -0.5516327888 -0.2458070838
                            [,5]          [,6]
ENSDARG00000043686 -0.0001414214 -0.0001414214
ENSDARG00000089089 -0.0001414214 -0.0001414214
ENSDARG00000060813 -0.9166316142  0.8705669273
ENSDARG00000092245  1.0161154062 -0.8659257909
ENSDARG00000094339 -0.0001414214 -0.0001414214
ENSDARG00000007918  0.4078691456 -0.2456914590
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>