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

By Antonio Scialdone, K. Natarajan, Luis R. Saraiva, V. Proserpio, Sarah A. Teichmann, Oliver Stegle, John C. Marioni, Florian Buettner. Methods, 2015

393 citations recorded by the source corpus.

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.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/8e3f4d4534fae2165a011d386b53eb0b7c592817

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Citations in the corpus

A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation. (2016, methodology): “Cell cycle category was inferred by using a recently described method.(35) To estimate absolute gene expression, external ERCC spike-ins were used to normalize reads within each plate by calculating spike-in size factors using the computeSpikeFactors function from the scran package before normalizin”
Structural Remodeling of the Human Colonic Mesenchyme in Inflammatory Bowel Disease (2018, methodology): “We determined cell-cycle scores for G1 and G2/M phases for each cell using a panel of gene pairs known to exhibit cell-cycle-stage-specific expression in murine cell lines (Scialdone et al., 2015).”
Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-Seq (2018, background): “GEPs corresponding to different phases of the cell cycle are examples of widespread activity programs and are well-known to confound identity (cell type) program inference in scRNA-Seq data (Chen and Zhou, 2017; Scialdone et al., 2015).”
Single-cell RNA-seq reveals ectopic and aberrant lung-resident cell populations in idiopathic pulmonary fibrosis (2020, methodology): “(41) to extract G1, G2-M, and S components from each individual cell before normalization using a set of 94 known cycle-associated genes.”
CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data (2016, methodology): “Several packages have recently been developed for the various aspects of scRNA-seq data analysis, including cell cycle (cyclone [3] and scLVM [4]), normalization (scran [5]), differential expression analysis (scde [2] and MAST [6]), and temporal analysis (Monocle[7]), but few perform preprocessing…”
Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures (2016, background): “Variation in cell-cycle stage causes large-scale changes in cell transcriptional states (Buettner et al. 2015, Scialdone et al. 2015, McDavid et al. 2014) — and we would expect it to induce stronger statistical dependencies amongst the affected genes.”