Data normalization and transformation using DESeq2
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@chiarafacciotto-18662
Last seen 17 months ago

Hi,

I am trying to use DESeq2 to perform normalization of my RNA-Seq data. I am wondering if, after estimating the size factors, I can directly run the rlog or vst transformation, or if I also need to use the count function in between. Basically I wonder which is the correct one between option 1 or option 2 of the codes reported below (in bold I highlighted the difference between the two scripts).

Thank you very much for your help!!!

Option 1:

  # Import data
  dds <- DESeqDataSetFromMatrix(countData = counts, colData = colData, design = ~ 1)

  # Pre-filtering
  dds <- dds[ rowSums(counts(dds)) > 0, ]

  # Estimate factor for normalization
  dds <- estimateSizeFactors(dds)

  # Compute log2 counts
  rld <- rlog(dds, blind=FALSE)
  table.out <- assay(rld)

Option 2:

  # Import data
  dds <- DESeqDataSetFromMatrix(countData = counts, colData = colData, design = ~ 1)

  # Pre-filtering
  dds <- dds[ rowSums(counts(dds)) > 0, ]

  # Estimate factor for normalization
  dds <- estimateSizeFactors(dds)
  dds <- counts(dds, normalized=TRUE)

  # Compute log2 counts
  rld <- rlog(dds, blind=FALSE)
  table.out <- assay(rld)
deseq2 normalization rnaseq rlog transformation variancestabilizingtransformation • 697 views
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@mikelove
Last seen 2 minutes ago
United States

The counts() function doesn't do any normalization, it just returns a matrix. So you don't want to do this: dds <- counts(dds, ...), because you've just replaced a DESeqDataSet (which has a lot of information in it) with a count matrix (which has less information). You've discarded all the information about genes and samples.

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Entering edit mode
@chiarafacciotto-18662
Last seen 17 months ago

Thanks! And is it ok to runĀ estimateSizeFactors(dss) before running rlog(dds, blind=FALSE)?

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Entering edit mode

Yes, you should. But if you haven't done that already, the rlog function will do that internally.

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