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5.4 years ago
MAPK ★ 2.0k

Hi All, I am working on RNAseq data analysis using DESeq2 R package. I havemy code dds <- DESeqDataSetFromMatrix(countData = count.mat , colData = cond, design = ~Strain + Time) to create the matrix. My confusion is with the design formula design = ~Strain + Time where I have Strain and Time variables to compare in my colData for my countData matrix.

This is my cond matrix

           Strain Time
count1           1    1
count2           1    1
count4           1    2
count5           1    2
count13          2    1
count14          2    1
count16          2    2
count17          2    2
countX           2    3


First, I want to understand the difference between these four designs that could go in the function DESeqDataSetFromMatrix:

a) design = ~Strain + Time + Strain:Time
b) design = ~Strain + Time
c) design = ~Time
and d) design = ~Strain


Second,

My understanding is that the DESeq2 takes the last variable in the design formula (here Time) as a control variable, so to test for different samples in Time group, I have these codes below. So, I want to know what the outputs of resultNames(ddsTC) really mean?

ddsTC <- DESeqDataSet(dds, ~ Strain + Time ) ##For time
ddsTC <- DESeq(ddsTC, test="LRT", reduced = ~Time )    #For Time

resultsNames(ddsTC)
[1] "Intercept.1"     "Time_3_vs_1.1"   "Time_2_vs_1.1"   "Strain_2_vs_1.1"

deseq2 • 23k views
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Hey, To the best of my knockledge A) design = ~Strain + Time means that deseq2 will test the effect of the Time (the last factor), controlling for the effect of Strain (the fi rst factor), so that the algorithm returns the fold change result only from the effect of time. B) design = ~Time here the algorithm will return the fold change that result from time without correcting for fold change that result from strain C) design = ~Strain same as above

So in my understanding Deseq2 treats the first factor as a co-variate and tries to eliminate the fold change that result because of this co-variate.

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Sorry to up this message but when we apply the design " design = ~Strain + Time ", how exactly deseq2 control the effect of Strain in order to test the Time ? It's not clear for me ..

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It's part of the GLM, that's how.

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Thank you so much for your answer. So what does design = ~Strain + Time + Strain:Time mean? Also, do you know what are the outputs of resultsNames(ddsTC) comparing?

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Regarding 'design = ~Strain + Time + Strain:Time ,

Here you added an interaction term (how time is interacting with stain in relation to regulation of gene expression). So this design will return the effect of time on the reference level of strain (1 or 2 depends on your setting). Using contrast () you can look for the effect of time on the other level in "strain"

Alternatively you can group the strain (with its different levels) and time ((with its different levels) into one factor, lets call it ALL. and by using contrast () you can look for the difference in log2 fold change between any combination of levels.

dds$ALL <- factor(paste0(dds$time, dds\$strain)) design(dds) <- ~ ALL

dds <- DESeq(dds) resultsNames(dds) results(dds, contrast=c("ALL","12", "11"))

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5.4 years ago

This ends up not being a questions about DESeq2, but about how linear models work (the same nomenclature is used for ANOVAs). tarek.mohamed basically explained this in the comments, but I'll point out that everything from limma to a standard ANOVA works exactly like this.

My understanding is that the DESeq2 takes the last variable in the design formula (here Time) as a control variable, so to test for different samples in Time group, I have these codes below. So, I want to know what the outputs of resultNames(ddsTC) really mean?

It doesn't take the last variable as a control, it just uses that one for plotting and output by default. So if you do design=Strain + Time and then do results() then the results will be for the Time effect.

resultNames() is giving you the names of the coefficients in your model.

ddsTC <- DESeqDataSet(dds, ~ Strain + Time ) ##For time
ddsTC <- DESeq(ddsTC, test="LRT", reduced = ~Time )    #For Time


Note that you're testing the effect of Strain above, not Time as suggested by the comment.

I strongly recommend familiarizing yourself with basic linear models. A pretty hefty chunk of the statistics used in bioinformatics boils down to that.

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Just posted this on twitter, but cross-posting here too:

The syntax is originally from here: https://www.jstor.org/stable/2346786?seq=1#metadata_info_tab_contents

It is sometimes called Wilkinson (& Rogers) syntax.

In the vignette, there are diagrams on interpreting interactions. A brief introduction to the design formula is in the workflow (which proceeds at a slower pace than the vignette). There are numerous design examples in ?results`. Users often miss the help pages, but these are a good resource for learning how the functions work.

Also see our edX material on linear models (Week 3):

http://rafalab.github.io/pages/harvardx.html

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Is there a new version of that class still offered?