title: Computational assignment of cell-cycle stage from single-cell transcriptome data.
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.
authors.affiliation: Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK; European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. Electronic address: as1@ebi.ac.uk.
authors.forename: Antonio
authors.initials: A
authors.lastname: Scialdone
authors.affiliation: Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK; European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.
authors.forename: Kedar N
authors.initials: KN
authors.lastname: Natarajan
authors.affiliation: Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK; European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.
authors.forename: Luis R
authors.initials: LR
authors.lastname: Saraiva
authors.affiliation: Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK; European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.
authors.forename: Valentina
authors.initials: V
authors.lastname: Proserpio
authors.affiliation: Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK; European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.
authors.forename: Sarah A
authors.initials: SA
authors.lastname: Teichmann
authors.affiliation: European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. Electronic address: stegle@ebi.ac.uk.
authors.forename: Oliver
authors.initials: O
authors.lastname: Stegle
authors.affiliation: Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK; European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. Electronic address: marioni@ebi.ac.uk.
authors.forename: John C
authors.initials: JC
authors.lastname: Marioni
authors.affiliation: European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK; Institute of Computational Biology, Helmholtz Zentrum München, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany. Electronic address: buettner@ebi.ac.uk.
authors.forename: Florian
authors.initials: F
authors.lastname: Buettner
mesh_terms: D000818:Animals; D002453:Cell Cycle / Q000502:physiology*; D045744:Cell Line, Tumor; D019295:Computational Biology / Q000379:methods; D053595:Embryonic Stem Cells / Q000502:physiology; D020869:Gene Expression Profiling / Q000379:methods*; D022781:Hepatocytes / Q000502:physiology; D006801:Humans; D000069550:Machine Learning; D051379:Mice; D059010:Single-Cell Analysis / Q000379:methods*; D059467:Transcriptome / Q000502:physiology*
keywords: Cell cycle; Computational biology; Machine learning; Single cell RNA-seq