Christopher Yau

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

This paper introduces a probabilistic model for inferring bifurcations in single-cell transcriptomics data and demonstrates its performance on large datasets.

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

This paper introduces probabilistic models to quantify uncertainty in pseudotime inference and shows they improve robustness in differential expression analysis compared to point estimates.

OncoPhase: Quantification of somatic mutation cellular prevalence using phase information

OncoPhase improves prevalence estimates of somatic mutations in heterogeneous tumors by incorporating phased germline variant information.

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

This paper introduces a Bayesian method using Gaussian Process Latent Variable Models to infer pseudotime in single-cell RNA-seq data, providing uncertainty estimates for pseudotimes.

pcaReduce: Hierarchical Clustering of Single Cell Transcriptional Profiles

pcaReduce integrates principal components analysis with hierarchical clustering to characterize cell state identity across multiple scales using single cell transcriptomics data.

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

ZIFA improves dimensionality reduction for single-cell RNA-seq data by explicitly modeling zero-inflated dropout events in simulated and biological datasets.