FastRNA: An efficient solution for PCA of single-cell RNA-sequencing data based on a batch-accounting count model.

ABSTRACT: Almost always, the analysis of single-cell RNA-sequencing (scRNA-seq) data begins with the generation of the low dimensional embedding of the data by principal-component analysis (PCA). Because scRNA-seq data are count data, log transformation is routinely applied to correct skewness prior to PCA, which is often argued to have added bias to data. Alternatively, studies have proposed methods that directly assume a count model and use approximately normally distributed count residuals for PCA. Despite their theoretical advantage of directly modeling count data, these methods are extremely slow for large datasets. In fact, when the data size grows, even the standard log normalization becomes inefficient. Here, we present FastRNA, a highly efficient solution for PCA of scRNA-seq data based on

SUBMITTER: Lee H 

PROVIDER: S-EPMC9674949 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature