Question: Normalize to additional variable (brain weight) in DESeq2
1
20 months ago by
mark.ebbert0 wrote:

Hi,

I'm working on a whole-tissue (brain) RNASeq study in mice where there is substantial neuronal death over time. We have multiple ages. I'd like to normalize to brain weight and would appreciate feedback to make sure I'm not doing anything that would violate DESeq2's internal modeling.

I perform the following steps:

1. Collect Conditional quantile normalization (CQN) for GC content and transcript length
2. Extract the offsets
3. Divide the exp(offsets) by brain weight (in grams)
4. Divide by the geometric mean

Questions:

2. Do you have any other suggestions?

Here is my code:

# Read in the saved length and GC content
mmu.len.gc <- mmu.len.gc[!is.na(mmu.len.gc$length) & !is.na(mmu.len.gc$gc),]

pre.dds <- estimateSizeFactors(pre.dds)

common_transcripts <- intersect(rownames(counts.all), rownames(mmu.len.gc))
counts.common <- counts.all[common_transcripts,]

# Perform conditional quantile normalization for GC and length. This will also
# account for library size.
cqn.obj <- cqn(counts=counts.common,
x=mmu.len.gc[common_transcripts,]$gc, lengths=mmu.len.gc[common_transcripts,]$length,
sizeFactors = sizeFactors(pre.dds))

# Extract offsets
cqnOffset.bw <- cqn.obj$glm.offset # Normalize to brain weight (converting from milligrams to grams) cqnNormFactors.bw <- exp(cqnOffset.bw) / (sample.sheet$brain.weight/1000)

# Divide by geometric
normFactors.bw <- cqnNormFactors.bw / exp(rowMeans(log(cqnNormFactors.bw)))

dds <- DESeqDataSetFromMatrix(countData = counts.common, colData = colData, design = ~age + sex + genotype)
normalizationFactors(dds) <- normFactors.bw 

Mark

modified 20 months ago by Michael Love24k • written 20 months ago by mark.ebbert0
3
20 months ago by
Michael Love24k
United States
Michael Love24k wrote:

Putting something as an offset (log of normalization factor) is like putting it in as a covariate but enforcing that the coefficient for each gene is equal to 1. I think it's safer here to just put brain weight as a covariate along with age and sex, and then it will be controlled for, for each gene.