Hi all,
We are running into a problem using an ANODEV with DESeq.

 

Let me state our data, our aim, and our dilemma. 



Our data (time course with various times titled A, B and C):

 
A design matrix would look something like this (this is a dramatic 
simplification, our real data contrains over 200 participants) :

Design <- data.frame(row.names=colnames(CountTable)
, timecourse = c("A","A","A","A","A","A", 
"B","B","B","B","B","B","C","C","C","C","C","C")
, pairs=c("1", "2", "3", "4", "5", "6","1", "2", "3", "4", "5","6","1", "2", 
"3", "4", "5", "6")
, condition=c("Control", "Control", "Control", "Disease", "Disease", 
"Disease","Control", "Control", "Control", "Disease", "Disease", 
"Disease","Control", "Control", "Control", "Disease", "Disease", "Disease"))
countTable <- CountTable
cds <- newCountDataSet(countTable, Design)
cds <- estimateSizeFactors(cds)
sizeFactors( cds )
cds <- estimateDispersions(cds, method="pooled-CR", fitType="local")

Our aim:



What we aim to do is to test for DE of transcripts across all 3 time 
points for disease and controls seperatly (using DESeq ANODEV) but we want 
to be able to identify at which 
time points these transcripts are being DE. In other words, we want to 
compare DE transcripts with 
respect to specific time points between cases and controls. Our remaining 
code looks like this:

fit0 <- fitNbinomGLMs (cds, count ~ timecourse)
fit1 <- fitNbinomGLMs ( cds, count ~ timecourse + condition )
str(fit1)

pvals <- nbinomGLMTest( fit1, fit0 )
padj <- p.adjust( pvals, method="fdr" )
rownames(counts(cds))[ padj < .1 ]



Our delimma:

ANODEV is in fact yielding a list of significantly differentially expressed 
genes. However, it is not informative if we want to know from which time 
point this differential expression occurred. From our knowledge there is no 
implementation of post-hoc testing(or equivilent) after 
ANODEV in DESeq that states at which time point a gene is being called 
significant from within a timecourse study. 

 

What insight can anyone offer us regarding DESeq towards better 
understanding 
at which time points are our DEGs are related to? 
In short, how can one tell at which time points (or both) is DESeq calling a 
transcript significantly DE?

Also, does this approach take into consideration the matching paired data?
Yours, 



Michael

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