[BioC] shangao

[wang peter]

how to design a model matrix
my data is composed of 35 samples, have two factor
time and treatment

i want to find DE genes cross the time, considering control
so do you think such coding is right?

how to get the DE from the lrt.ede

raw.data <- read.table("expression-table.txt",row.names=1)
lib_size <- read.table("lib_size.txt");
lib_size <- unlist(lib_size)
d <- DGEList(counts = raw.data, lib.size = lib_size)
dge <- d[rowSums(d$counts) >= length(lib_size)/2,]

#normalization
dge <- calcNormFactors(dge)

treatment=factor(c(rep('control',6),rep('treated',24),rep('control',5)))
time=factor(c('0h','0h','0h','24h','24h','24h','0h','0h','0h','6h','6h','6h','6h','12h','12h','12h','12h','18h','18h','18h','18h',
             '24h','24h','24h','36h','36h','36h','48h','48h','48h','6h','12h','18h','24h','36h'))
design <- model.matrix(~time+treatment*time)

dge <- estimateGLMCommonDisp(dge, design)
dge <- estimateGLMTagwiseDisp(dge, design)
glmfit.dge <- glmFit(dge, design,dispersion=dge$common.dispersion)
lrt.dge <- glmLRT(dge, glmfit.dge, coef=2)


-- 
shan gao
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