I am using DESeq2 for a while now, but I never see people talking about the use of the function "estimateSizeFactors" with the option  "controlGenes".

For what I understand, it is supposed to be used with spike-ins, in which case DESeq2 will estimate the size factors only with the spike-ins and use it to normalize the counts of the other genes. 

In my case, I am suspecting a global shift in gene expression, so is it correct to use internal controls (instead of spike-ins) in the option "controlGenes"? Or would you say that a global shift negates the premise of the algorithm?

Usage: 

• ## S4 method for signature 'DESeqDataSet'
  estimateSizeFactors(object, type = c("ratio",
    "poscounts", "iterate"), locfunc = stats::median, geoMeans,
    controlGenes, normMatrix, quiet = FALSE)

Example Code Looks Like:

• dds <- makeExampleDESeqDataSet(n=1000, m=4)
  dds <- estimateSizeFactors(dds)
  sizeFactors(dds)
  
  dds <- estimateSizeFactors(dds, controlGenes=1:200)
  
  m <- matrix(runif(1000 * 4, .5, 1.5), ncol=4)
  dds <- estimateSizeFactors(dds, normMatrix=m)
  normalizationFactors(dds)[1:3,]
  
  geoMeans <- exp(rowMeans(log(counts(dds))))
  dds <- estimateSizeFactors(dds,geoMeans=geoMeans)
  sizeFactors(dds)

This would be the Helpful link for you. 

Check this out: https://rdrr.io/bioc/DESeq2/man/estimateSizeFactors.html

Regards;

Visit for Dog Sneezing Issues.

 I know lots of groups that use this argument with endogenous features for example housekeeping promoters or genes.  This is part of its intended usage