residuals.DGEGLM: Deviance and Pearson Residuals for the Negative Binomial...

In drisso/RUVSeq: Remove Unwanted Variation from RNA-Seq Data

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

object	An object of class DGEGLM as created by the glmFit function of edgeR.
type	Compute deviance or Pearson residuals.
...	Additional arguments to be passed to the generic function.

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)

drisso/RUVSeq documentation built on Nov. 25, 2022, 5:55 a.m.