[Bioc-devel] DESeq2 time series - How to set the experimental design

[Nadia Kamal]

Hi,

I am trying to analyze RNA-Seq Data with DESeq2 and could use some help. 
I have 2 genotypes and 14 timepoints. I want to find differences in gene 
expression between both genotypes overall and at every timepoint and 
between every two timepoints in each genotype.

Here is what I did so far.

library("DESeq2")

directory<-"............."
sampleFiles <- grep("??",list.files(directory),value=TRUE)

time <- factor(c("T12", "T13", "T14", "T1", "T11", "T5", "T7", "T2", 
"T9", "T3", "T6", "T8", "T4", "T10", "T12", "T13", "T14", "T1", "T11", 
"T5", "T7", "T2", "T9", "T3", "T6", "T8", "T4", "T10" ))
genotype <- 
factor(c("GF","GF","GF","GF","GF","GF","GF","GF","GF","GF","GF","GF","GF","GF","VB","VB","VB","VB","VB","VB","VB","VB","VB","VB","VB","VB","VB","VB"))
sampleTable<-data.frame(sampleName=sampleFiles, fileName=sampleFiles,  
genotype=genotype, time=time)
ddsHTSeq <- DESeqDataSetFromHTSeqCount(sampleTable=sampleTable, 
directory=directory, design=~genotype+time+time:genotype)

ddsHTSeq$genotype<-factor(ddsHTSeq$genotype, levels=c("GF","VB"))
ddsHTSeq$time <- factor(ddsHTSeq$time, levels=c("T1", "T2", "T3", "T4", 
"T5", "T6", "T7", "T8", "T9", "T10", "T11", "T12", "T13", "T14"))

dds <- DESeq(ddsHTSeq)
res <- results(dds)

ddsHTSeq <- estimateSizeFactors(ddsHTSeq)
ddsHTSeq <- estimateDispersions(ddsHTSeq)
ddsLRT <- nbinomLRT(dds, reduced = formula(~time+genotype))

or ~genotype + time in the full and ~time in the reduced formula, but I 
think this is not suitable for my experiment. When I use the first 
design and do plotMA, I have no significant genes at all, so I must be 
doing something wrong. I would be greatfull for some help. Thank you.

 > sessionInfo()
R version 3.1.1 (2014-07-10)
Platform: x86_64-redhat-linux-gnu (64-bit)

locale:
  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8
  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C
  [9] LC_ADDRESS=C               LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

attached base packages:
[1] parallel  stats     graphics  grDevices utils     datasets methods
[8] base

other attached packages:
[1] DESeq2_1.4.5              RcppArmadillo_0.4.450.1.0
[3] Rcpp_0.11.2               GenomicRanges_1.16.4
[5] GenomeInfoDb_1.0.2        IRanges_1.22.10
[7] BiocGenerics_0.10.0

loaded via a namespace (and not attached):
  [1] annotate_1.42.1      AnnotationDbi_1.22.6 Biobase_2.20.1
  [4] DBI_0.2-5            genefilter_1.46.1    geneplotter_1.42.0
  [7] grid_3.1.1           lattice_0.20-29      locfit_1.5-9.1
[10] RColorBrewer_1.0-5   RSQLite_0.11.2       splines_3.1.1
[13] stats4_3.1.1         survival_2.37-7      XML_3.98-1.1
[16] xtable_1.7-1         XVector_0.4.0


-- 
Nadia Kamal
Bielefeld University
Center for Biotechnology (Cebitec)
Genome Research
Universitätsstraße 27
33615 Bielefeld
Germany