DE analysis on Single cell RNA Seq time course data (using pseudotime) with 5 different genotypes
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Entering edit mode
rj1435 • 0
@8f54760c
Last seen 10 days ago
United States

Hi,

I am trying to conduct a DE analysis comparing my wildtype single cell data with 4 other genotypes. If there is a change in gene expression in at least 1 of the 4 genotypes compared to WT than that gene is considered DE. We ran the following code taking into account time course however the only output is one padj/p value for each gene, which I assume is the average across all 5 genotypes. However, I want a padj/p value for each gene for each genotypes so I can compare my WT to the others. Any suggestions on how to do this?

Code should be placed in three backticks as shown below

time_dynam_deseq <- function(pt) {
  metadata <- data.frame(timepoint = pt$timepoint, strain = pt$strain, pseudotime = pt$slingPseudotime_1)
  rownames(metadata) <- rownames(colData(pt))

  dds <- DESeqDataSetFromMatrix(countData = as.matrix(counts(pt) + 1),
                                colData = metadata,
                                design = ~strain* pseudotime)

  dds <- DESeq(dds, test = "Wald")
  dds
}

dds_strains <- unique(dds$strain)
isolated_strains <- list()
for (strain in dds_strains) {
  isolated_strains[[strain]] <- subset(dds, strain == strain)
}

#For example, both strain results yield the same values
dmd3_res <- results(dds, contrast=c("strain", "dmd-3(-)", "WT"))
wt_res <- results(dds, contrast=c("strain", "WT", "dmd-3(-)"))

# include your problematic code here with any corresponding output 
# please also include the results of running the following in an R session 

sessionInfo( )
DESeq2 • 183 views
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Entering edit mode
@james-w-macdonald-5106
Last seen 4 days ago
United States

The two contrasts at the bottom are identical but for the sign of the logFC, so you should expect the same results (except for flipped sign of the logFC).

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