**10**wrote:

**3.6k**• written 7.9 years ago by Miguel Gallach •

**10**

Question: Block x Treatment interaction test with DeSeq

0

Miguel Gallach • **10** wrote:

Hi list,
I am interested in testing Genetic x Environment interaction for RNA-
Seq
data. However, if I understood correctly, DeSeq do not test for
interaction, right?
My experimental design consists in comparing expression of two
different
populations, two replicates per population (Pop A1, Pop A2 vs. Pop B1
and
Pop B2), at different temperatures (T1 and T2). The next table
represents
my data frame and my procedure:
myData:
A1.T1 A2.T1 B1.T1
B2.T1
A1.T2 A2.T2 B1.T2
B2.T2
Gene1 count count count
count count count
count
count
Gene2 count count count
count count count
count
count
Gene n count count count
count count count
count
count
Desing = data.frame (treatment = ("T1"," T1"," T1"," T1"," T2"," T2","
T2"," T2"), block = c("A1","A2","B1","B2","A1","A2","B1","B2"))
cds = newCountDataSet(myData, desing)
cds = estimateSizeFactors(cds)
cds = estimateVarianceFunctions(cds, method = "pooled")
fit0 = nbinomFitGLM(cds, count ~ block)
fit1 = nbinomFitGLM(cds, count ~ block + treatment)
fit2 = nbinomFitGLM(cds, count ~ block + treatment + block:treatment)
pvals2 = nbinomGLMTest (fit2, fit1)
padj2 = p.adjust (pvals2, method = "BH")
pvals = nbinomGLMTest (fit1, fit0)
padj = p.adjust (pvals, method = "BH")
myData = data.frame (myData, pvals, padj)
May I use the same cds to test fit0, fit1 and fit2?
If understand ok, this is not correct since I need to estimate
different
SizeFactor and VarianceFuntions for each GLM, wright?
If I am wright, is then there anyway to test for Block, treatments and
Block:Treatment with DeSeq?
Thank you very much for your help.
Miguel
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modified 7.9 years ago
by
Simon Anders • **3.6k**
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written
7.9 years ago by
Miguel Gallach • **10**

Answer: Block x Treatment interaction test with DeSeq

0

Simon Anders • **3.6k** wrote:

Hi Miguel
On 11/28/2011 10:16 AM, Miguel Gallach wrote:
> I am interested in testing Genetic x Environment interaction for
RNA-Seq
> data. However, if I understood correctly, DeSeq do not test for
> interaction, right?
Of course, DESeq can test for interaction.
> My experimental design consists in comparing expression of two
different
> populations, two replicates per population (Pop A1, Pop A2 vs. Pop
B1 and
> Pop B2), at different temperatures (T1 and T2). The next table
represents
> my data frame and my procedure:
You table got a bis messed up, so I rather write down how I understood
your design data to look like.
library population temperature
A1.T1 A T1
A2.T1 A T1
B1.T1 B T1
B2.T1 B T1
A1.T2 A T2
A2.T2 A T2
B1.T2 B T2
B2.T2 B T2
This is now assuming that by different "population", you mean
something
like different genotype or ecotype or colony, and that of either of
these, you have four aliquots/cultures/etc, two of which you let grow
at
temperature T1 and the other two at T2.
If you give DESeq the columns 'population' and 'temperature' as a
'design' data frame and the call 'estimateDispersions' with
'method="pooled"', it will consider those sample pairs which have the
same population and temperature values as replicates and estimate
dispersions accordingly. Then, you can fit models like
fit0 <- nbinomFitGLM(cds, count ~ population)
fit1 <- nbinomFitGLM(cds, count ~ population + treatment )
fit2 <- nbinomFitGLM(cds, count ~ population + treatment +
population:treatment )
The last one is equivalent to
fit2 <- nbinomFitGLM(cds, count ~ population * treatment )
Now depending on what you want to test, you may for example do
pvals <- nbinomGLMTest( fit1, fit0 )
if you consider 'population' as a blocking factor (i.e., a nuisance
covariate whose influence you want to get rid of), and
pvals <- nbinomGLMTest( fit2, fit1 )
if you consider both factors as biologically interesting and want to
see
their interactions.
Simon

Dear Simon - I know this is a very late follow up - but there is a reason I need to relate directly to this post.

I am interested in replicating this (i.e. ~ population + treatment + population:treatment, in my case ~ gender + disease + gender:disease, 22 samples in total (each one from a different subject)) with DESeq2. DESeq2 appears to no longer use nbinomFitGLM - does the function have a new name in DESeq2, or is the approach now different?

Could you take me through it step by step from the cds object ( I make it using DESeqDataSetFromHTSeqCount)

cds <- DESeqDataSetFromHTSeqCount(sampleTable = sampleTable, directory = directory, design = ~gender+disease) cds <- DESeq(cds)

My disease factor has 2 levels: ASD and LTC, and I'm interested in how the disease effects may be different in male and female samples. I have 11 in each of the diesase groups: ASD = 9x male, 2x female, LTC = 4x male, 7x female)

I tried to follow the steps in the manual for standard and expanded model matrices, but got quickly lost.

Thanks,

Matt

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