[BioC] how to design a model matrix and test by edgeR

[wang peter]

how to design a model matrix and test by edgeR
dear all:
my data is composed of 35 samples, have two factor
time and treatment

1. The first question is
i want to find DE genes (6h.treatment-0h.treatment)-(6h.control-0h.control)
how to design the model? please tell me if the design and test is right
such is my coding.

2. the second question is
should i use samples involved 6h and 0h to do glmFitting
or use all of 35 samples to do?
i think i should use all

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)
#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','36h','48h'))
design <- model.matrix(~time*treatment)

dge <- estimateGLMCommonDisp(dge, design)
dge <- estimateGLMTagwiseDisp(dge, design)
glmfit.dge <- glmFit(dge, design,dispersion=dge$common.dispersion)
lrt.dge <- glmLRT(dge, glmfit.dge, coef=13)
result <- topTags(lrt.dge, adjust.method="BH", sort.by="logFC")

-- 
shan gao
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Boyce Thompson Institute
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