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Scialdone, A.* ; Natarajan, K.N.* ; Saraiva, L.R.* ; Proserpio, V.* ; Teichmann, S.A.* ; Stegle, O.* ; Marioni, J.C.* ; Buettner, F.

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

Methods 85, 54-61 (2015)
Postprint DOI PMC
Open Access Green
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 normalization 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 normalization 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 normalization, is it possible to robustly capture the transcriptional cell-cycle signature across different cell types, organisms and experimental protocols.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Cell Cycle ; Computational Biology ; Machine Learning ; Single Cell; Rna-seq; Gene-expression; Stem-cells; Heterogeneity; Dynamics; Cancer
ISSN (print) / ISBN 1046-2023
e-ISSN 1095-9130
Journal Methods
Quellenangaben Volume: 85, Issue: , Pages: 54-61 Article Number: , Supplement: ,
Publisher Elsevier
Publishing Place Amsterdam [u.a.]
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)