Hi, I want to perform differential expression analyses of multiple RNA-seq samples using DESeq. We have included ERCC spike in controls and wish to use these to normalise the count data.

I have seen a few posts that suggest I use estimateSizeFactors() on a DESeqDataSet consisting of only the ERCC RNAs then apply these size factors to the DESeqDataSet containing my experimental data.

We have used the same total amount of RNA and spike in volume for each sample so there are no corrections applied first. However, we have used mix1 in our treatment samples and mix2 in our control. Would it make more sense then to only use subgroup B of the ERCC spike ins to estimate size factors as these are the same concentration in both mixes?

Is there perhaps a more accurate way to go about this? I have read the "Synthetic spike-in standards for RNA-seq experiments" paper which suggests plotting expected fpkm fold change against observed and fitting a curve. However, I would prefer to use DESeq and count based differential expression to compare with previous analyses performed without spike in.

Thanks in advance for any help with this.

I have seen a few posts that suggest I use estimateSizeFactors() on a DESeqDataSet consisting of only the ERCC RNAs then apply these size factors to the DESeqDataSet containing my experimental data.

We have used the same total amount of RNA and spike in volume for each sample so there are no corrections applied first. However, we have used mix1 in our treatment samples and mix2 in our control. Would it make more sense then to only use subgroup B of the ERCC spike ins to estimate size factors as these are the same concentration in both mixes?

Is there perhaps a more accurate way to go about this? I have read the "Synthetic spike-in standards for RNA-seq experiments" paper which suggests plotting expected fpkm fold change against observed and fitting a curve. However, I would prefer to use DESeq and count based differential expression to compare with previous analyses performed without spike in.

Thanks in advance for any help with this.

## Comment