Evaluating imputation methods for single-cell RNA-seq data.

ABSTRACT:

Background

Single-cell RNA sequencing (scRNA-seq) enables the high-throughput profiling of gene expression at the single-cell level. However, overwhelming dropouts within data may obscure meaningful biological signals. Various imputation methods have recently been developed to address this problem. Therefore, it is important to perform a systematic evaluation of different imputation algorithms.

Results

In this study, we evaluated 11 of the most recent imputation methods on 12 real biological datasets from immunological studies and 4 simulated datasets. The performance of these methods was compared, based on numerical recovery, cell clustering and marker gene analysis. Most of the methods brought some benefits on numerical recovery. To some extent, the performance of imputati

SUBMITTER: Cheng Y 

PROVIDER: S-EPMC10386301 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature