Deleted:DESeq2 design with 6 groups
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teddy1 • 0
Last seen 4 weeks ago

I have data composed of 3 different controls and 3 different treatments each in triplicates. The data is represented below for easier understanding:

sample         condition     condition_by_groups
controlA_1     untreated     untreated_A
controlA_2     untreated     untreated_A
controlA_3     untreated     untreated_A
controlB_1     untreated     untreated_B
controlB_2     untreated     untreated_B
controlB_3     untreated     untreated_B
controlC_1     untreated     untreated_C
controlC_2     untreated     untreated_C
controlC_3     untreated     untreated_C

treatedA_1     treated       treated_A
treatedA_2     treated       treated_A
treatedA_3     treated       treated_A
treatedB_1     treated       treated_B
treatedB_2     treated       treated_B
treatedB_3     treated       treated_B
treatedC_1     treated       treated_C
treatedC_2     treated       treated_C
treatedC_3     treated       treated_C

A, B, C represents the different controls or treatments and the numbers 1, 2 and 3 the biological repeats.

I would like to analyze the difference in gene expression in the following way:

  1. treated vs untreated (disregarding the fact that controls and treatments are different)
  2. pairwise by groups (e.g. treated_A vs untreated_A; treated_B vs untreated_B, etc.)

I have setup the design like this:

coldata <- data.frame(
   sample = c( "controlA_1", "controlA_2", "controlA_3", "controlB_1", "controlB_2", "controlB_3", "controlC_1", "controlC_2", "controlC_3", 
                "treatedA_1", "treatedA_2", "treatedA_3", "treatedB_1", "treatedB_2", "treatedB_3", "treatedC_1", "treatedC_2", "treatedC_3" ),

   condition = c( "untreated", "untreated", "untreated", "untreated", "untreated", "untreated", "untreated", "untreated", "untreated",
                    "treated", "treated", "treated", "treated", "treated", "treated", "treated", "treated", "treated" ), 

   condition_by_groups = c( "untreated_A", "untreated_A", "untreated_A", "untreated_B", "untreated_B", "untreated_A", "untreated_C", "untreated_C", "untreated_C",
                                "treated_A", "treated_A", "treated_A", "treated_B", "treated_B", "treated_B", "treated_C", "treated_C", "treated_C" ), 
  row.names = "sample" )

coldata$condition <- as.factor(coldata$condition)
coldata$condition_by_groups <- as.factor(coldata$condition_by_groups)

#construct DESeqDataSet1
dds <- DESeqDataSetFromMatrix(countData = count_matrix, colData = coldata, 
                              design = ~ condition)

dds <- DESeq(dds)

res0 <- results(dds, contrast=c("condition", "treated", "untreated"))

#construct DESeqDataSet2
dds <- DESeqDataSetFromMatrix(countData = count_matrix, colData = coldata, 
                              design = ~ condition_by_groups)

dds <- DESeq(dds)

res1 <- results(dds, contrast = c('condition_by_groups', 'treated_A', 'untreated_A'))
res2 <- results(dds, contrast = c('condition_by_groups', 'treated_B', 'untreated_B'))
res3 <- results(dds, contrast = c('condition_by_groups', 'treated_C', 'untreated_C'))

Is it appropriate for the analyses I want to perform or something needs to be modified?

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