Benchmarking imputation methods for network inference using a novel method of synthetic scRNA-seq data generation

ABSTRACT:

Background

Single cell RNA-sequencing (scRNA-seq) has very rapidly become the new workhorse of modern biology providing an unprecedented global view on cellular diversity and heterogeneity. In particular, the structure of gene-gene expression correlation contains information on the underlying gene regulatory networks. However, interpretation of scRNA-seq data is challenging due to specific experimental error and biases that are unique to this kind of data including drop-out (or technical zeros).

Methods

To deal with this problem several methods for imputation of zeros for scRNA-seq have been developed. However, it is not clear how these processing steps affect inference of genetic networks from single cell data. Here, we introduce Biomodelling.jl, a tool for generation of

SUBMITTER: Lasri A 

PROVIDER: S-EPMC9204969 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature