Network modeling to infer sparse gene co-expression estimates from single-cell sequencing data

Single-cell level estimation of gene co-expression is non-trivial because of the inherent sparsity of single-cell sequencing data. This study aims to survey, evaluate, and compare single-cell co-expression estimation methods by applying them to a public cancer single-cell RNA-seq dataset (Peng et al. Nature 2019). I evaluated three methods, scLink, locCSN, and COTAN on their efficacy in dealing with sparse single-cell gene co-expression data