Regulatory network-based imputation of dropouts in single-cell RNA sequencing data

Abstract

Single-cell RNA sequencing (scRNA-seq) methods are typically unable to quantify the expression levels of all genes in a cell, creating a need for the computational prediction of missing values ('dropout imputation'). Most existing dropout imputation methods are limited in the sense that they exclusively use the scRNA-seq dataset at hand and do not exploit external gene-gene relationship information.Here, we show that a gene regulatory network learned from external, independent gene expression data improves dropout imputation. Using a variety of human scRNA-seq datasets we demonstrate that our network-based approach outperforms published state-of-the-art methods. The network-based approach performs particularly well for lowly expressed genes, including cell-type-specific transcriptional regulators. Additionally, we tested a baseline approach, where we imputed missing values using the sample-wide average expression of a gene. Unexpectedly, 52% to 77% of the genes were better predicted using this baseline approach, suggesting negligible cell-to-cell variation of expression levels for many genes. Our work shows that there is no single best imputation method; rather, the best method depends on gene-specific features, such as expression level and expression variation across cells. We thus implemented an R-package called ADImpute (available from http://cellnet.cecad.uni-koeln.de/adimpute ) that determines the best imputation method for each gene in a dataset.

A CRISPR/Cas9-based enhancement of high-throughput single-cell transcriptomics

“…As a result, only a small fraction of the nascent mRNA is processed, potentially confounding biological interpretation and reproducibility. While computational approaches are being developed to combat this problem inherent to scRNAseq [23][24][25] , an effective molecular solution remains to be elucidated.…”