wald.test: Wald test for performing DE analysis
In mixtNB: DE Analysis of RNA-Seq Data by Mixtures of NB

Description Usage Arguments Details Value Author(s) References Examples

This function implements the Wald test for performing DE according to three statistics: difference, ratio and logratio

1	wald.test(out, statistic = "diff", quiet = FALSE, alpha = 0.01)

`out`	The fit of mixtNB
`statistic`	The statistic to be used: "diff" (difference, the default), "ratio" and "logratio"
`quiet`	Logical to indicate if the DE genes should be printed
`alpha`	the significance level to detect DE genes

This function implements the Wald test for performing DE according to three statistics: difference, ratio and logratio. It returns the statistics, the p-values and the adjusted p-values according to the Benjamini and Hochberg (1995)

A list containing

`stat`	The value of the Wald test
`pvalue`	nominal p-values for each gene
`pvalueadj`	adjusted p-values according to the Benjamini and Hochberg (1995)
`var`	estimated variances of the genes
`gname`	Positions of the filtered genes

Elisabetta Bonafede, Cinzia Viroli

E. Bonafede, F. Picard, S. Robin and C. Viroli (2015), Modelling overdispersion heterogeneity in differential expression analysis using mixtures, under revision.

lambda.de<-matrix(runif(100,0,250),100)
lambda.de=cbind(lambda.de,lambda.de/exp(rnorm(100,0.5,0.125)))
lambda<-rbind(lambda.de,matrix(runif(900,0,250),900,2))
a<-runif(1000,0.5,600)
cr<-rep(1:2,each=5)
y<-matrix(0,1000,10)
for (i in 1:1000) for (l in 1:10) y[i,l]<-rnbinom(1,mu=lambda[i,cr[l]],size=a[i])
fit=fit.mixtNB(y,cr,K=3)
DE.genes=wald.test(fit)