Question: Deseq2 group comparison
gravatar for Hari
13 months ago by
Hari0 wrote:


I have an experimental set up as described below.

               cell     dex
S.control1         s1 control
S.control2         s2 control
Scontrol3          s3 control
S.condition1.1     s4     Sus
S.condition1.2     s5     Sus
S.condition1.3     s6     Sus
S.condition2.1     s7    Sus1
S.condition2.1.1   s8    Sus1
S.condition2.1.2   s9    Sus1
R.control1.1      s10 R1_ctrl
R.control1.2      s11 R1_ctrl
R.control1.2.1    s12 R1_ctrl
R.condition1.1    s13      R1
R.condition1.2    s14      R1
R.condition1.3    s15      R1
R.control2.1      s16 R2_ctrl
R.control2.2      s17 R2_ctrl
R.control2.2.1    s18 R2_ctrl
R.condition2.1    s19      R2
R.condition2.2    s20      R2
R.condition2.3    s21      R2

I have same one control for all Susceptible (S) where are there are two resistance (R1 & R2) for which i have two different controls.  because resistance species are different but the comparison i have is for the orthologous genes between all the three species.  My idea is to compare between the control Vs. treated for each conditions but also mainly to understand the genes that are differentially expressed from S Vs R. Ainc ei know they share the same function, i would like to know which ones are different? 

what i tried so far is

dat= read.csv("test_eff_counts.csv", header = TRUE, row.names = 1)

coldata<-data.frame(row.names=colnames(dat),cell=paste0("s", 1:21), dex= c(rep('control', 3), rep('Sus',3), rep('Sus1',3),rep('R1_ctrl',3),rep('R1',3),rep('R2_ctrl',3), rep('R2',3)))

coldata$cell<-factor(coldata$cell,ordered = T)

dds <- DESeqDataSetFromMatrix(countData = round(dat), colData = coldata, design = ~ dex)

dds <- dds[ rowSums(counts(dds)) > 1, ] #remove zero rows
dds<- DESeq(dds)
results(dds,contrast = c("condition","control","Sus")) (continued for all four treated of control vs, treated)

This just give me pair wise comparisons. but how do i understand the genes that are differentially expressed from S Vs R

Any help would be greatly appreciated!!!! :(

ADD COMMENTlink modified 13 months ago by Michael Love20k • written 13 months ago by Hari0
gravatar for Michael Love
13 months ago by
Michael Love20k
United States
Michael Love20k wrote:

I don't exactly follow the experiment. Can you make a sample table where the columns are treatment (control/treated) and condition or species? And can you say more precisely what you want to find? Genes in which the treatment effect differs across condition/species?

ADD COMMENTlink written 13 months ago by Michael Love20k
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