  The cell cycle is one of the most fundamental biological processes important
for understanding normal physiology and various pathologies such as cancer.
Single cell RNA sequencing technologies give an opportunity to analyse the cell
cycle transcriptome dynamics in an unprecedented range of conditions (cell
types and perturbations), with thousands of publicly available datasets. Here
we review the main computational tasks in such analysis: 1) identification of
cell cycle phases, 2) pseudotime inference, 3) identification and profiling of
cell cycle-related genes, 4) removing cell cycle effect, 5) identification and
analysis of the G0 (quiescent) cells. We review seventeen software packages
that are available today for the cell cycle analysis using scRNA-seq data.
Despite huge progress achieved, none of the packages can produce complete and
reliable results with respect to all aforementioned tasks. One of the major
difficulties for existing packages is distinguishing between two patterns of
cell cycle transcriptomic dynamics: normal and characteristic for embryonic
stem cells (ESC), with the latter one shared by many cancer cell lines.
Moreover, some cell lines are characterized by a mixture of two subpopulations,
one following the standard and one ESC-like cell cycle, which makes the
analysis even more challenging. In conclusion, we discuss the difficulties of
the analysis of cell cycle-related single cell transcriptome and provide
certain guidelines for the use of the existing methods.
