Evaluating the Reproducibility of Single-Cell Gene Regulatory Network Inference Algorithms.

ABSTRACT: Networks are powerful tools to represent and investigate biological systems. The development of algorithms inferring regulatory interactions from functional genomics data has been an active area of research. With the advent of single-cell RNA-seq data (scRNA-seq), numerous methods specifically designed to take advantage of single-cell datasets have been proposed. However, published benchmarks on single-cell network inference are mostly based on simulated data. Once applied to real data, these benchmarks take into account only a small set of genes and only compare the inferred networks with an imposed ground-truth. Here, we benchmark six single-cell network inference methods based on their reproducibility, i.e., their ability to infer similar networks when applied to two independent dataset

SUBMITTER: Kang Y 

PROVIDER: S-EPMC8019823 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature