Removing unwanted variation from large-scale RNA sequencing data with PRPS - Nature Biotechnology

Overall, our results illustrate that RUV-III shows a very satisfactory performance in a situation where PRPS is poorly chosen.We assessed the performance of RUV-III with PRPS in situations where the biological labels are partially known . To simulate such situations, we used one of the CMS subtypes, CMS4, to create PRPS for RUV-III normalization of the TCGA READ RNA-seq data. Note that this subtype is not present across all the plates.

We proposed an approach, called PRPS, to deploy RUV-III for normalization of RNA-seq in situations where suitable technical replicates are not available. Our PRPS approach requires the presence of at least a homogenous biological population across sources of unwanted variation. Then, we create pseudo-samples by averaging gene expression of a group of samples that are roughly homogeneous regarding the unwanted variation and biology.

In the TCGA READ RNA-seq study, noticeable library size differences between samples remained in the FPKM and FPKM.UQ normalized data due to the presence of genes whose raw counts showed weak or negative association with library size. In such situations, normalizations that rely on a global scale factor can introduce, rather than remove, library size variation. We found this issue in several TCGA cancer studies, even those that used a single plate for profiling.

Research Briefing: Normalizing cancer RNA-seq data for library size, tumor purity and batch effects

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