Group comparison in DESeq2
2
2
Entering edit mode
@jjinhyoungkim-8476
Last seen 5.6 years ago
Canada

Hi,

I am trying to run DESeq2 with the duplicated raw counts from 4 groups and 2 conditions using the  two-way ANOVA design below. Referring a vignette of DESeq2, various comparisons have been run as below. Besides of these results, I want to get DEGs between groups (group1 vs each other group). How can get DEGs between groups in each condition? I tried to run following comparisons, but it did not get reasonable number of DEGs. Do I need to make other design for these comparisons between groups? Thank you in advance for your valuable comments.

Jin

 

>library("DESeq2")
>countMatrix = read.table("~/Desktop/aaa.txt",header = T,row.names = 1)
>coldata = data.frame(row.names =colnames(countMatrix),group =rep(c("gt1","gt2","gt3","gt4"),2,each = 2),treatment = rep(c("control","treated"),each= 8))
>coldata$treatment = factor(x = coldata$treatment,levels = c('control','treated'))>dds = DESeqDataSetFromMatrix(countData =countMatrix, colData = coldata, design = ~ group + treatment +group:treatment)
>dds = DESeq(dds)
> resultsNames(dds)
 [1] "Intercept"       "groupgt1"       "groupgt2"       "groupgt3"                 [5] "groupgt4"   "treatmentcontrol"    "treatmenttreated" "groupgt1.treatmentcontrol"
 [9] "groupgt2.treatmentcontrol" "groupgt3.treatmentcontrol" "groupgt4.treatmentcontrol" "groupgt1.treatmenttreated"
[13] "groupgt2.treatmenttreated" "groupgt3.treatmenttreated" "groupgt4.treatmenttreated"

# the main effect for condition

>res <- results(dds,contrast=list("treatmenttreated","treatmentcontrol"))
#the interaction term for condition in each group
>res <- results(dds, contrast=list("groupgt1.treatmenttreated","groupgt1.treatmentcontrol"))
>res <- results(dds,
contrast=list("groupgt2.treatmenttreated","groupgt2.treatmentcontrol"))
>res <- results(dds,
contrast=list("groupgt3.treatmenttreated","groupgt3.treatmentcontrol"))
>res <- results(dds,
contrast=list("groupgt4.treatmenttreated","groupgt4.treatmentcontrol")) 
# the condition effect in each goup
>res <- results(dds,
contrast=list(c("treatmenttreated","groupgt1.treatmenttreated"),
c("treatmentcontrol","groupgt1.treatmentcontrol")))
>res <- results(dds,
contrast=list(c("treatmenttreated","groupgt2.treatmenttreated"),
c("treatmentcontrol","groupgt2.treatmentcontrol")))
>res <- results(dds,
contrast=list(c("treatmenttreated","groupgt3.treatmenttreated"),
c("treatmentcontrol","groupgt3.treatmentcontrol")))
>res <- results(dds,
contrast=list(c("treatmenttreated","groupgt4.treatmenttreated"),
c("treatmentcontrol","groupgt4.treatmentcontrol"))) 

 

## The comparison between groups

# group1 vs other group

>res <- results(dds, contrast=list("groupgt2","groupgt1"))
>res <- results(dds, contrast=list("groupgt3","groupgt1"))
>res <- results(dds, contrast=list("groupgt4","groupgt1"))

# In the control condition
>res <- results(dds,
contrast=list("groupgt2.treatmentcontrol","groupgt1.treatmentcontrol"))
>res <- results(dds,
contrast=list("groupgt3.treatmentcontrol","groupgt1.treatmentcontrol"))
>res <- results(dds,
contrast=list("groupgt4.treatmentcontrol","groupgt1.treatmentcontrol"))

# In the treated condition
>res <- results(dds,
contrast=list("groupgt2.treatmenttreated","groupgt1.treatmenttreated"))
>res <- results(dds,
contrast=list("groupgt3.treatmenttreated","groupgt1.treatmenttreated"))
>res <- results(dds,
contrast=list("groupgt4.treatmenttreated","groupgt1.treatmenttreated"))

# treated vs control
>res <- results(dds,
contrast=list("groupgt2.treatmenttreated","groupgt1.treatmentcontrol"))
>res <- results(dds,
contrast=list("groupgt3.treatmenttreated","groupgt1.treatmentcontrol"))
>res <- results(dds,
contrast=list("groupgt4.treatmenttreated","groupgt1.treatmentcontrol"))
 
deseq2 results • 15k views
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3
Entering edit mode
@mikelove
Last seen 1 day ago
United States

In order to compare the different groups in each condition, I recommend to follow the advice from another post:

A: DESEq2 comparison with mulitple cell types under 2 conditions

This will make it easy for you to contrast any groups you want.

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0
Entering edit mode

Hi Michael Love,

Great! Thanks for your help as always.

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0
Entering edit mode

I tried to use the group comparison and got a error message as below. How can be it fixed?


> coldata
                    set condition
Genotype1.1         gt1   control
Genotype1.2         gt1   control
Genotype2.1         gt2   control
Genotype2.2         gt2   control
Genotype3.1         gt3   control
Genotype3.2         gt3   control
Genotype4.1         gt4   control
Genotype4.2         gt4   control
Genotype1.1_treated gt1   treated
Genotype1.2_treated gt1   treated
Genotype2.1_treated gt2   treated
Genotype2.2_treated gt2   treated
Genotype3.1_treated gt3   treated
Genotype3.2_treated gt3   treated
Genotype4.1_treated gt4   treated
Genotype4.2_treated gt4   treated

> dds$group <- factor(paste0(dds$set, dds$condition))
Error in `[[<-`(`*tmp*`, name, value = integer(0)) :
  0 elements in value to replace 16 elements

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0
Entering edit mode

You haven't constructed dds yet so you can't operate on its columns. You can either first make dds or you can perform this operation on coldata columns:

coldata$group <- factor(paste0(coldata$set, coldata$condition))
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0
Entering edit mode
@mikelove
Last seen 1 day ago
United States

post changed to comment above

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0
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Here is update what I did. I will try to run group comparison, if it is correct. Thanks again.

 

> coldata = data.frame(row.names = colnames(countMatrix),set = rep(c("gt1","gt2","gt3","gt4"),2,each = 2),condition = rep(c("control","treated"),each = 8))
>
> coldata$condition = factor(x = coldata$condition,levels = c('control','treated'))
>
> dds = DESeqDataSetFromMatrix(countData = countMatrix, colData = coldata, design = ~ set + condition + set:condition)
> coldata
                    set condition
Genotype1.1         gt1   control
Genotype1.2         gt1   control
Genotype2.1         gt2   control
Genotype2.2         gt2   control
Genotype3.1         gt3   control
Genotype3.2         gt3   control
Genotype4.1         gt4   control
Genotype4.2         gt4   control
Genotype1.1_treated gt1   treated
Genotype1.2_treated gt1   treated
Genotype2.1_treated gt2   treated
Genotype2.2_treated gt2   treated
Genotype3.1_treated gt3   treated
Genotype3.2_treated gt3   treated
Genotype4.1_treated gt4   treated
Genotype4.2_treated gt4   treated
> coldata$group <- factor(paste0(coldata$set, coldata$condition))
> dds$group <- factor(paste0(dds$set, dds$condition))
>

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