Question: How is the design in DESeq2 work?
0
gravatar for MAPK
4 weeks ago by
MAPK1.1k
United States
MAPK1.1k wrote:

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 • 190 views
ADD COMMENTlink modified 4 weeks ago by Devon Ryan73k • written 4 weeks ago by MAPK1.1k
1

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.

ADD REPLYlink written 4 weeks ago by tarek.mohamed110

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?

ADD REPLYlink written 4 weeks ago by MAPK1.1k
1

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"))

ADD REPLYlink modified 4 weeks ago • written 4 weeks ago by tarek.mohamed110
2
gravatar for Devon Ryan
4 weeks ago by
Devon Ryan73k
Freiburg, Germany
Devon Ryan73k wrote:

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.

ADD COMMENTlink written 4 weeks ago by Devon Ryan73k
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