  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.
