BEARscc determines robustness of single-cell clusters using simulated technical replicates

Nature Communications
David Tyler SeversonBenjamin Schuster-Böckler

Abstract

Single-cell messenger RNA sequencing (scRNA-seq) has emerged as a powerful tool to study cellular heterogeneity within complex tissues. Subpopulations of cells with common gene expression profiles can be identified by applying unsupervised clustering algorithms. However, technical variance is a major confounding factor in scRNA-seq, not least because it is not possible to replicate measurements on the same cell. Here, we present BEARscc, a tool that uses RNA spike-in controls to simulate experiment-specific technical replicates. BEARscc works with a wide range of existing clustering algorithms to assess the robustness of clusters to technical variation. We demonstrate that the tool improves the unsupervised classification of cells and facilitates the biological interpretation of single-cell RNA-seq experiments.

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Citations

May 19, 2020·Briefings in Bioinformatics·Yanhong HuangXiaoping Liu
Feb 9, 2020·Genome Biology·David LähnemannAlexander Schönhuth
Jun 14, 2020·Cells·Weikang Nicholas LinLih Feng Cheow
Dec 30, 2020·BMC Genomics·Adam CornishChittibabu Guda

Related Concepts

Metazoa
RNA
Sequence Determinations, RNA
MRNA Differential Display
Mouse, Swiss
Single-Cell Analysis
Classification
Gene Expression
RNA
RNA, Messenger

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