Resistant Fit Regression Normalization for Single-cell RNA-seq Data

All mRNA quantification, including single-cell mRNA sequencing, requires normalization to correct for technical variation and to make measurements of two or more samples comparable. The choice of normalization method impacts the downstream analysis. All common approaches (applying scaling factors, variational inference, and quantile regression) currently focus on removing technical variations but ignore localized variations of biological origin. To address this problem, we propose a new framework to normalize for technical effects while also aligning RNA-seq datasets for a biologically meaningful comparison. We present an iterative optimization method using the notion of a resistant fit regression to isolate localized perturbations. Both simulated data and real data are resistant-fit normalized and compared with popular normalization methods. This comparison shows that the resistant fit works better under localized biological variations.