dEMObuODlmTest: dEMO differential expression analysis from RNA-seq data sets.
In emodoro/dEMO: Differential Expression and confidencial estimation for FC

Description Usage Arguments Value Author(s) Examples

Differential Expression Analysis dEMO, which assums counts from RNA-seq experiments fit two different negative binomial distributions, as dEMO test method explains. Overdisperssion parameter is estimated by estimateTagwiseDisp.default function.

1 2	dEMObuODlmTest(expr, condition, original = NULL, alpha = 0.05, method.adj = "BH")

`expr`	data.frame, ExpressionSet or matrix after dataset has been filtered
`condition`	Binary vector where 0 means control and 1 treatment
`original`	data.frame, ExpressionSet or matrix before dataset has been filtered
`alpha`	significance level for hypothesis test. Default value is 0.05
`method.adj`	It is the multiple testing correction method. Default value correspond to Benjamini-Hochberg correction. c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY","fdr", "none")

A data.frame is returned, which cointains log2FC, PVALUE, ADJ.PVAL (adjusted p value) and dEMO.STAT (statistic of dEMO test)

Enrique Perez_Riesgo

library(compcodeR)
library(edgeR)
set.seed(123456987)
datasCI <- generateSyntheticData(dataset = dEMOresults, n.vars = 15000,
samples.per.cond = 6, n.diffexp = 1500,repl.id = 1, seqdepth = 1e7,
fraction.upregulated = 0.5, between.group.diffdisp = FALSE,
filter.threshold.total = 1, filter.threshold.mediancpm = 0,
fraction.non.overdispersed = 0)
expressiondata <- datasCI@count.matrix
TMMfac <- calcNormFactors.default(expressiondata, method = "TMM")
exprT <- t(t(expressiondata)*TMMfac)
conditions <- (datasCI@sample.annotations$condition - 1)
testdEMOTMM <- dEMObuODlmTest(expr = exprT, condition = conditions,
original = exprT)