Tuning parameters of dimensionality reduction methods for single-cell RNA-seq analysis.

ABSTRACT:

Background

Many computational methods have been developed recently to analyze single-cell RNA-seq (scRNA-seq) data. Several benchmark studies have compared these methods on their ability for dimensionality reduction, clustering, or differential analysis, often relying on default parameters. Yet, given the biological diversity of scRNA-seq datasets, parameter tuning might be essential for the optimal usage of methods, and determining how to tune parameters remains an unmet need.

Results

Here, we propose a benchmark to assess the performance of five methods, systematically varying their tunable parameters, for dimension reduction of scRNA-seq data, a common first step to many downstream applications such as cell type identification or trajectory inference. We run a total of 1.

SUBMITTER: Raimundo F 

PROVIDER: S-EPMC7444048 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature