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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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter 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
Zeitschrift Methods
Quellenangaben Band: 85, Heft: , Seiten: 54-61 Artikelnummer: , Supplement: ,
Verlag Elsevier
Verlagsort Amsterdam [u.a.]
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)