I am trying to design an RNA-seq experiment which would detect global shift in transcript abundance between control and treated cells (we know by other measures that we may expect such shift at least for a larger amount of transcripts).
I am considering using ERCC spike-ins followed by normalization with RUVSeq Normalization of RNA-seq data using factor analysis of control genes or samples. I am not sure which ERCC kit configuration will be the most suitable for my experiment. The ERCC ExFold Spike-In Mixes contains Mix1 and Mix2 with different molar ratios of certain groups of transcripts to assess the accuracy of differential gene expression measurements (which sounds like a more suitable option for my experiment), as oppose to just one type of Mix (Mix1) in the other Kit, which according to Thermo helps to determine dynamic range and lower limit of detection.
Now my question is how would I make a use of different concentrations of spike-ins (when using Mix1 and Mix2) in my samples to perform normalization using RUVg? As far as I understand this approach uses negative control genes which have same abundance between different samples. Although in the Bioconductor RUVSeq vignette authors suggest that
one can relax the negative control gene assumption by requiring instead the identification of a set of positive or negative controls, with a priori known expression fold-changes between samples, i.e., known β. One can then use the centered counts for these genes (log Y − Xβ) for normalization purposes.
I cannot think how to incorporate known fold changes for transcripts within spike-in Mix1 and Mix2 into RUVSeq R analysis. Does anyone have any suggestions? Many thanks!
