scTenifoldKnk: a machine learning workflow performing virtual knockout experiments on single-cell gene regulatory networks

Abstract

Bigger PictureO_LIGene knockout (KO) experiments, using genetically altered animals, are a proven powerful approach to elucidate the role of a gene in a biological process. However, systematic KO experiments targeting many genes are usually prohibitive due to limited experimental and animal resources. Here, we present scTenifoldKnk, an efficient virtual KO tool that allows the systematic deletion of many genes individually. scTenifoldKnk uses single-cell RNA sequencing (scRNAseq) data from wild-type (WT) samples to predict gene function in a cell type-specific manner. We show that predictions made by scTenifoldKnk recapitulate findings from real-animal KO experiments. scTenifoldKnk has proven to be a powerful and effective approach for elucidating gene function, prioritizing KO targets, predicting experimental outcomes before real-animal KO experiments are conducted. C_LI HighlightsO_LIscTenifoldKnk performs virtual KO experiments using scRNAseq data. C_LIO_LIscTenifoldKnk only requires data from WT samples; no data is needed from KO samples. C_LIO_LIPredictions made by scTenifoldKnk recapitulate findings from real-animal KO experiments. C_LI Data Science Maturity Level O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=14 SRC="FIGDIR/small/436484v2_ufig1.gif" ALT="Figure 1"> View larger version (8K): org.highwire.dtl.DTLVardef@157126aorg.highwire.dtl.DTLVardef@17a2e76org.highwire.dtl.DTLVardef@805c4dorg.highwire.dtl.DTLVardef@12bdc1b_HPS_FORMAT_FIGEXP M_FIG C_FIG eTOC blurbscTenifoldKnk is a machine learning workflow performing virtual KO experiments to predict gene function. It constructs gene regulatory networks using single-cell RNA sequencing data from wild-type samples and then computationally deletes target genes. Real-data applications demonstrate that scTenifoldKnk recapitulates findings of real-animal KO experiments and accurately predicts gene function in analyzed cells. SummaryGene knockout (KO) experiments are a proven, powerful approach for studying gene function. However, systematic KO experiments targeting a large number of genes are usually prohibitive due to the limit of experimental and animal resources. Here, we present scTenifoldKnk, an efficient virtual KO tool that enables systematic KO investigation of gene function using data from single-cell RNA sequencing (scRNAseq). In scTenifoldKnk analysis, a gene regulatory network (GRN) is first constructed from scRNAseq data of wild-type samples, and a target gene is then virtually deleted from the constructed GRN. Manifold alignment is used to align the resulting reduced GRN to the original GRN to identify differentially regulated genes, which are used to infer target gene functions in analyzed cells. We demonstrate that the scTenifoldKnk-based virtual KO analysis recapitulates the main findings of real-animal KO experiments and recovers the expected functions of genes in relevant cell types.