Identifying and removing the cell-cycle effect from single-cell RNA-Sequencing data

TL;DR

This paper introduces ccRemover, a novel method for accurately identifying and removing cell-cycle effects from single-cell RNA sequencing data, thereby improving cell type clustering without losing other biological signals.

Contribution

The paper presents ccRemover, a new technique that effectively isolates and eliminates cell-cycle bias in scRNA-Seq data, outperforming existing methods in preserving biological signals.

Findings

ccRemover reliably identifies cell-cycle effects in scRNA-Seq data.

Applying ccRemover improves clustering accuracy of cell types.

The method preserves other biological signals while removing cell-cycle bias.

Abstract

Single-cell RNA-Sequencing (scRNA-Seq) is a revolutionary technique for discovering and describing cell types in heterogeneous tissues, yet its measurement of expression often suffers from large systematic bias. A major source of this bias is the cell cycle, which introduces large within-cell-type heterogeneity that can obscure the differences in expression between cell types. The current method for removing the cell-cycle effect is unable to effectively identify this effect and has a high risk of removing other biological components of interest, compromising downstream analysis. We present ccRemover, a new method that reliably identifies the cell-cycle effect and removes it. ccRemover preserves other biological signals of interest in the data and thus can serve as an important pre-processing step for many scRNA-Seq data analyses. The effectiveness of ccRemover is demonstrated using simulation data and three real scRNA-Seq datasets, where it boosts the performance of existing clustering algorithms in distinguishing between cell types.