2019/03/19 by Christof Seiler, Lisa M. Kronstad, Seiler, Christof +11
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #Applications (stat.AP) #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Single-cell and spatial transcriptomics #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #stat.AP
paper · pdf · doi:10.48550/arxiv.1903.07976
arxiv created 2019/03/19 · openalex publication_date 2019/03/19 · arxiv updated 2019/03/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Mass cytometry technology enables the simultaneous measurement of over 40 proteins on single cells. This has helped immunologists to increase their understanding of heterogeneity, complexity, and lineage relationships of white blood cells. Current statistical methods often collapse the rich single-cell data into summary statistics before proceeding with downstream analysis, discarding the information in these multivariate datasets. In this article, our aim is to exhibit the use of statistical analyses on the raw, uncompressed data thus improving replicability, and exposing multivariate patterns and their associated uncertainty profiles. We show that multivariate generative models are a valid alternative to univariate hypothesis testing. We propose two models: a multivariate Poisson log-normal mixed model and a logistic linear mixed model. We show that these models are complementary and that either model can account for different confounders. We use Hamiltonian Monte Carlo to provide Bayesian uncertainty quantification. Our models applied to a recent pregnancy study successfully reproduce key findings while quantifying increased overall protein-to-protein correlations between first and third trimester.