scTenifoldKnk: An Efficient Virtual Knockout Tool for Gene Function Predictions via Single-Cell Gene Regulatory Network Perturbation
96 Pages Posted: 23 Jun 2021 Publication Status: PublishedMore...
Gene knockout (KO) experiments are a proven approach for studying gene function. A typical KO experiment usually involves the phenotypic characterization of KO organisms. The recent advent of single-cell technology has greatly boosted the resolution of cellular phenotyping. Applications of single-cell technology in KO experiments hold promises for providing unprecedented insights into gene functions. However, the large-scale application of single-cell technology in systematic KO experiments is prohibitive due to the vast resources required. Here we present scTenifoldKnk—an efficient software tool that performs virtual KO—using single-cell RNA sequencing (scRNAseq) data. In a scTenifoldKnk virtual KO analysis, a single-cell gene regulatory network (scGRN) is first constructed from the scRNAseq data of the wild-type (WT) samples. Then, a target gene is knocked out from the adjacency matrix of constructed scGRN by setting weights of the gene’s outward edges to zeros. This “pseudo-KO” scGRN is compared with the original WT scGRN to identify significantly differentially regulated (DR) genes. We call these DR genes virtual-KO perturbed genes, which are used to infer functions of the KO gene in analyzed cells. Using existing data sets, we demonstrate that the scTenifoldKnk-based virtual KO analysis recapitulates the main findings of real-animal KO experiments and recovers the expected functions of causal genes of Mendelian diseases in relevant cell types. Finally, we demonstrate the use of scTenifoldKnk to perform systematic KO analyses, in which a large number of genes are individually deleted, or a single gene is deleted in a large number of tissues and cell types.
Keywords: virtual knockout, gene knockout, gene function prediction, single-cell RNA sequencing, gene regulatory network, functional genomics, unsupervised machine learning
Suggested Citation: Suggested Citation