Comprehensive evaluation of noise reduction methods for single-cell RNA sequencing data.

ABSTRACT: Normalization and batch correction are critical steps in processing single-cell RNA sequencing (scRNA-seq) data, which remove technical effects and systematic biases to unmask biological signals of interest. Although a number of computational methods have been developed, there is no guidance for choosing appropriate procedures in different scenarios. In this study, we assessed the performance of 28 scRNA-seq noise reduction procedures in 55 scenarios using simulated and real datasets. The scenarios accounted for multiple biological and technical factors that greatly affect the denoising performance, including relative magnitude of batch effects, the extent of cell population imbalance, the complexity of cell group structures, the proportion and the similarity of nonoverlapping cell populat

SUBMITTER: Chu SK 

PROVIDER: S-EPMC8921632 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature