Why does DESeq2 give negative log2 fold change in treatment vs control for some genes when normalized counts are higher in treatment.
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sropri • 0
@af8dfadb
Last seen 4 days ago
United States

Hi,

I have ran DESeq2 to get log2 fold changes of hypertrophy samples relative to control. (Added my code below). I think I am setting my contrast correct of Hypertrophy vs. control correctly while getting results. My log2 fold change results are mostly in concordance with what I observe in normalized counts. Meaning, for a gene, if the control samples have a lower mean than the hypertrophy samples, they show a positive log fold change and vice versa However, for some genes the results are conflicting. For example, The gene ATP5D is shown to have a negative log2 fold change of -0.48 but when I look at the normalized counts of the gene, the gene clearly has higher mean in the hypertrophy samples than the control (shown below). Or LSM7 has a negative log2 fold change of -0.5 while the normalized counts of Hypertrophy samples show a higher mean. On the other hand DSC3 has a negative log2 fold change of -2.13 and that can be observed in the normalized counts (shown below). Would appreciate any insight into why this might occur. I have also included a picture of my metadata.metadata


 # Create DESeq2 object        
  dds <- DESeqDataSetFromMatrix(counts, 
                                colData = md, 
                                design = ~PCNAscore + batch + group)


  # Run DESeq2 differential expression analysis
  dds <- DESeq(dds)

# Check the coefficients for the comparison
  resultsNames(dds)

  # Generate results object
  #res <- results(dds)
  res <- results(dds, contrast=c("group","HYPER","CTRL"), alpha = 0.05)

[1] "Intercept"              "PCNAscore"              "batch_batch2_vs_batch1" "batch_batch3_vs_batch1"
[5] "group_HYPER_vs_CTRL"

  # Shrink the log2 fold changes to be more appropriate using the apeglm method - should cite [paper]() when using this method
  res <- lfcShrink(dds, 
                   coef = "group_HYPER_vs_CTRL",
                   res=res,
                   type = "apeglm")
  res_tbl <- res %>%
    data.frame() %>%
    rownames_to_column(var = "gene") %>%
    as_tibble() %>%
    arrange(padj)

sessionInfo( )

Plot of ATP5D normalized counts in control and hypertrophy samples

Plot of LSM7 normalized counts in control and hypertrophy samples

Plot of DSC3 normalized counts in control and hypertrophy samples

DESeq2 • 1.0k views
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@mikelove
Last seen 1 day ago
United States

If you have other covariates, you can't compare the counts directly. The batch and score influence the expected counts.

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Entering edit mode

That makes sense. I am running iterations without any confounders, and with confounders to see how much of an effect each has. Appreciate your help.

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