The shaky foundations of simulating single-cell RNA sequencing data.

ABSTRACT:

Background

With the emergence of hundreds of single-cell RNA-sequencing (scRNA-seq) datasets, the number of computational tools to analyze aspects of the generated data has grown rapidly. As a result, there is a recurring need to demonstrate whether newly developed methods are truly performant-on their own as well as in comparison to existing tools. Benchmark studies aim to consolidate the space of available methods for a given task and often use simulated data that provide a ground truth for evaluations, thus demanding a high quality standard results credible and transferable to real data.

Results

Here, we evaluated methods for synthetic scRNA-seq data generation in their ability to mimic experimental data. Besides comparing gene- and cell-level quality control summaries in

SUBMITTER: Crowell HL 

PROVIDER: S-EPMC10061781 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature