Elsevier

Methods

Volume 85, 1 September 2015, Pages 54-61
Methods

Computational assignment of cell-cycle stage from single-cell transcriptome data

https://doi.org/10.1016/j.ymeth.2015.06.021Get rights and content
Under a Creative Commons license
open access

Highlights

  • Machine learning based approaches allow cell cycle stage to be predicted from single-cell RNA-sequencing data.

  • Appropriate normalisation and use of prior information is critical.

  • Among the tested methods, only the PCA-based and the custom-built predictors are able to robustly capture a generalizable cell-cycle signature in the transcriptome.

Abstract

The transcriptome of single cells can reveal important information about cellular states and heterogeneity within populations of cells. Recently, single-cell RNA-sequencing has facilitated expression profiling of large numbers of single cells in parallel. To fully exploit these data, it is critical that suitable computational approaches are developed. One key challenge, especially pertinent when considering dividing populations of cells, is to understand the cell-cycle stage of each captured cell. Here we describe and compare five established supervised machine learning methods and a custom-built predictor for allocating cells to their cell-cycle stage on the basis of their transcriptome. In particular, we assess the impact of different normalisation strategies and the usage of prior knowledge on the predictive power of the classifiers. We tested the methods on previously published datasets and found that a PCA-based approach and the custom predictor performed best. Moreover, our analysis shows that the performance depends strongly on normalisation and the usage of prior knowledge. Only by leveraging prior knowledge in form of cell-cycle annotated genes and by preprocessing the data using a rank-based normalisation, is it possible to robustly capture the transcriptional cell-cycle signature across different cell types, organisms and experimental protocols.

Keywords

Single cell RNA-seq
Computational biology
Cell cycle
Machine learning

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