Normalization of Single-cell RNA-seq Data Using Partial Least Squares with Adaptive Fuzzy Weight

Abstract

AbstractNormalization of single-cell RNA-seq (scRNA-seq) is a crucial step in downstream analysis, where raw data are adjusted to correct unwanted factors that prevent the direct comparison of expression measures. scRNA-seq data exhibits a multivariate relationship between transcript-specific expression and sequencing depth that a single scale factor cannot address. A partial least squares (PLS) regression was performed to accommodate the variability of gene expression in each condition, and upper and lower quantiles with adaptive fuzzy weights were utilized to correct unwanted biases in scRNA-seq data. The present approach was compared using real and simulated datasets across various state-of-the-art performance measures.