Biology subjects

Christopher Yau

Publications and source records attributed to Christopher Yau.

8 recordsLinked to original sources

bioRxiv2016

Probabilistic inference of bifurcations in single-cell data using a hierarchical mixture of factor analysers

Modelling 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 analysers. Our model exhibits competitive performance on large datasets despite implementing full MCMC sampling and its unique hierarchical prior structure enables automatic determination of genes driving the bifurcation process.

Bioinformatics↗

bioRxiv2016

Ouija: Incorporating prior knowledge in single-cell trajectory learning using Bayesian nonlinear factor analysis

Pseudotime estimation from single-cell gene expression allows the recovery of temporal information from otherwise static profiles of individual cells. This pseudotemporal information can be used to characterise transient events in temporally evolving biological systems. Conventional algorithms typically emphasise an unsupervised transcriptome-wide approach and use retrospective analysis to evaluate the behaviour of individual genes. Here we introduce an orthogonal approach termed \"Ouija\" that learns pseudotimes from a small set of marker genes that might ordinarily be used to retrospectively confirm the accuracy of unsupervised pseudotime algorithms. Crucially, we model these genes in terms of switch-like or transient behaviour along the trajectory, allowing us to understand why the pseudotimes have been inferred and learn informative parameters about the behaviour of each gene. Since each gene is associated with a switch or peak time the genes are effectively ordered along with the cells, allowing each part of the trajectory to be understood in terms of the behaviour of certain genes. In the following we introduce our model and demonstrate that in many instances a small panel of marker genes can recover pseudotimes that are consistent with those obtained using the entire transcriptome. Furthermore, we show that our method can detect differences in the regulation timings between two genes and identify \"metastable\" states - discrete cell types along the continuous trajectories - that recapitulate known cell types. Ouija therefore provides a powerful complimentary approach to existing whole transcriptome based pseudotime estimation methods. An open source implementation is available at http://www.github.com/kieranrcampbell/ouija as an R package and at http://www.github.com/kieranrcampbell/ouijaflow as a Python/TensorFlow package.

Bioinformatics↗

bioRxiv2016

Order under uncertainty: robust differential expression analysis using probabilistic models for pseudotime inference

Single cell gene expression profiling can be used to quantify transcriptional dynamics in temporal processes, such as cell differentiation, using computational methods to label each cell with a pseudotime where true time series experimentation is too difficult to perform. However, owing to the high variability in gene expression between individual cells, there is an inherent uncertainty in the precise temporal ordering of the cells. Preexisting methods for pseudotime ordering have predominantly given point estimates precluding a rigorous analysis of the implications of uncertainty. We use probabilistic modelling techniques to quantify pseudotime uncertainty and propagate this into downstream differential expression analysis. We demonstrate that reliance on a point estimate of pseudotime can lead to inflated false discovery rates compared and that probabilistic approaches provide greater robustness and measures of the temporal resolution that can be obtained from pseudotime inference.

Bioinformatics↗

bioRxiv2016

OncoPhase: Quantification of somatic mutation cellular prevalence using phase information

The impact of evolutionary processes in cancer and its implications for drug response, biomarker validation and clinical outcome requires careful consideration of the evolving mutational landscape of the cancer. Genome sequencing allows us to identify mutations but the prevalence of those mutations in heterogeneous tumours must be inferred. We describe a method that we call OncoPhase to compute the prevalence of somatic point mutations from genome sequencing analysis of heterogeneous tumours that combines information from nearby phased germline variants. We show using simulations that the use of phased germline information can give improved prevalence estimates over the use of somatic variants only.

Bioinformatics↗

bioRxiv2015

Bayesian Gaussian Process Latent Variable Models for pseudotime inference in single-cell RNA-seq data

Single-cell genomics has revolutionised modern biology while requiring the development of advanced computational and statistical methods. Advances have been made in uncovering gene expression heterogeneity, discovering new cell types and novel identification of genes and transcription factors involved in cellular processes. One such approach to the analysis is to construct pseudotime orderings of cells as they progress through a particular biological process, such as cell-cycle or differentiation. These methods assign a score - known as the pseudotime - to each cell as a surrogate measure of progression. However, all published methods to date are purely algorithmic and lack any way to give uncertainty to the pseudotime assigned to a cell. Here we present a method that combines Gaussian Process Latent Variable Models (GP-LVM) with a recently published electroGP prior to perform Bayesian inference on the pseudotimes. We go on to show that the posterior variability in these pseudotimes leads to nontrivial uncertainty in the pseudo-temporal ordering of the cells and that pseudotimes should not be thought of as point estimates.

Bioinformatics↗

bioRxiv2015

pcaReduce: Hierarchical Clustering of Single Cell Transcriptional Profiles

MotivationAdvances in single cell genomics provides a way of routinely generating transcriptomics data at the single cell level. A frequent requirement of single cell expression experiments is the identification of novel patterns of heterogeneity across single cells that might explain complex cellular states or tissue composition. To date, classical statistical analysis tools have being routinely applied to single cell data, but there is considerable scope for the development of novel statistical approaches that are better adapted to the challenges of inferring cellular hierarchies.\n\nResultsHere, we present a novel integration of principal components analysis and hierarchical clustering to create a framework for characterising cell state identity. Our methodology uses agglomerative clustering to generate a cell state hierarchy where each cluster branch is associated with a principal component of variation that can be used to differentiate two cellular states. We demonstrate that using real single cell datasets this approach allows for consistent clustering of single cell transcriptional profiles across multiple scales of interpretation.\n\nAvailabilityR implementation of pcaReduce algorithm is available from https://github.com/JustinaZ/pcaReduce

Bioinformatics↗

bioRxiv2015

ZIFA: Dimensionality reduction for zero-inflated single cell gene expression analysis

Single cell RNA-seq data allows insight into normal cellular function and diseases including cancer through the molecular characterisation of cellular state at the single-cell level. Dimensionality reduction of such high-dimensional datasets is essential for visualization and analysis, but single-cell RNA-seq data is challenging for classical dimensionality reduction methods because of the prevalence of dropout events leading to zero-inflated data. Here we develop a dimensionality reduction method, (Z)ero (I)nflated (F)actor (A)nalysis (ZIFA), which explicitly models the dropout characteristics, and show that it improves modelling accuracy on simulated and biological datasets.

Bioinformatics↗

bioRxiv2014

Accounting for sources of bias and uncertainty in copy number-based statistical deconvolution of heterogeneous tumour samples

Deconvolving heterogeneous tumour samples to identify constituent cell populations with differing copy number profiles using whole genome sequencing data is a challenging problem. Copy number calling algorithms have differential detection rates for different sizes and classes of copy number alterations. This paper describes how uncertainty in classification and differential detection rates can introduce biases in measures of clonal diversity. A simulation strategy is introduced that allows differential detection rates to be adjusted for and this process is shown to minimise bias.

Bioinformatics↗