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Old 05-29-2014, 02:03 PM   #1
id0
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Default DESeq2 multiple levels

I loaded a set of samples into DESeq2 with one factor that has three levels. There is a single condition I called "Type" with three levels A, B, C.

This is what I did:
Code:
> ddsHTSeq <- DESeqDataSetFromHTSeqCount(sampleTable = sampleTable, directory = directory, design = ~ Type)
> dds <- DESeq(ddsHTSeq, betaPrior=FALSE)
> resultsNames(dds)
 [1] "Intercept"             "Type_B_vs_A"    "Type_C_vs_A"
I can use contrasts to run any of the pairwise comparisons. What if I want to get the differences between all three factors? Is that possible? What is the proper approach for that?
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Old 05-30-2014, 08:40 AM   #2
Michael Love
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See the LRT section of the main vignette:

Quote:
3.6 Likelihood ratio test

One reason to use the likelihood ratio test is in order to test the null hypothesis that log2 fold changes for multiple levels of a factor ... are equal to zero.
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Old 05-30-2014, 12:22 PM   #3
id0
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Thanks for pointing that out. I didn't notice that before.

The example output is:
Code:
## log2 fold change: condition treated vs untreated
## LRT p-value: '~ condition' vs '~ 1'
## DataFrame with 2 rows and 6 columns
##             baseMean log2FoldChange lfcSE     stat      pvalue    padj
##             <numeric><numeric>      <numeric> <numeric> <numeric> <numeric>
## FBgn0000003   0.159  15.0447        195.974   0.791     0.374     NA
## FBgn0000008   52.226 0.0281         0.298     0.010     0.920     0.971
It says p-value is "'~ condition' vs '~ 1'", but what exactly does that mean?
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Old 05-30-2014, 12:31 PM   #4
Michael Love
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The likelihood ratio test is a standard test, I'd recommend consulting a statistics textbook.

Or you can also start here:

http://en.wikipedia.org/wiki/Likelihood-ratio_test

~ condition is what we call the "full" model, it is also called the alternative model

~ 1 is what we call the "reduced" model, it is also called the null model

Here we are testing whether the explanatory power (in terms of the likelihood of the observed data) of including the condition information in the model, when compared to the null model (~ 1 means we fit only an intercept term, i.e., all samples belong to the same group), is more than expected by chance alone.
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