Abstract
Modeling bifurcations in single-cell transcriptomics data has become an increasingly popular field of research. Several methods have been proposed to infer bifurcation structure from such data, but all rely on heuristic non-probabilistic inference. Here we propose the first generative, fully probabilistic model for such inference based on a Bayesian hierarchical mixture of factor analyzers. Our model exhibits competitive performance on large datasets despite implementing full Markov-Chain Monte Carlo sampling, and its unique hierarchical prior structure enables automatic determination of genes driving the bifurcation process. We additionally propose an Empirical-Bayes like extension that deals with the high levels of zero-inflation in single-cell RNA-seq data and quantify when such models are useful. We apply or model to both real and simulated single-cell gene expression data and compare the results to existing pseudotime methods. Finally, we discuss both the merits and weaknesses of such a unified, probabilistic approach in the context practical bioinformatics analyses.
Highlights
Trajectory analysis of single-cell RNA-seq data has become a popular method that attempts to infer lost temporal information, such as a cell’s differentiation state1,2
We propose the first generative, fully probabilistic model for such inference based on a Bayesian hierarchical mixture of factor analyzers
principal component analysis (PCA) representations of the synthetic data can be seen in Figures 2A and B, showing the characteristic Y
Summary
Trajectory analysis of single-cell RNA-seq (scRNA-seq) data has become a popular method that attempts to infer lost temporal information, such as a cell’s differentiation state. Trajectory analysis of single-cell RNA-seq (scRNA-seq) data has become a popular method that attempts to infer lost temporal information, such as a cell’s differentiation state1,2 Such analyses reconstruct a measure of a cell’s progression through some biological process, known as a pseudotime. Several methods have been proposed to infer bifurcation structure from single-cell data. While DPT arguably has a probabilistic interpretation, neither method specifies a fully generative model that incorporates measurement noise, while both infer bifurcations retrospectively after constructing pseudotimes. A further algorithm Monocle learns pseudotimes based on dimensionality reduction using the DDRTree algorithm and provides post-hoc inference of genes involved in the bifurcation process using generalized linear models
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