University of Edinburgh
School of Mathematics and BioSS
Date: Friday 17th Feb, 15:10 Location: JCMB 6201
Speaker: Catalina Vallejos, UCL and Alan Turing Institute
Title: BASiCS: Bayesian Analysis of Single Cell Sequencing data
Abstract: Recently, single-cell mRNA sequencing (scRNA-seq) has emerged as a novel tool for quantifying gene expression profiles of individuals cells. These assays can provide novel insights into a tissue's function and regulation. However, besides experimental issues, statistical analysis of scRNA-seq data is itself a challenge. In particular, a prominent feature of scRNA-seq experiments is strong measurement error. This is reflected by (i) technical dropouts, where a gene is expressed in a cell but its expression is not captured through sequencing and (ii) poor correlation between expression measurements of technical replicates. Critically, these effects must be taken into account in order to reveal biological findings that are not confounded by technical variation.
In this talk I introduce BASiCS (Bayesian Analysis of Single-Cell Sequencing data) [1,2], an integrative approach to jointly infer biological and technical effects in scRNA-seq datasets. It builds upon a Bayesian hierarchical modelling framework, based on a Poisson formulation. BASiCS uses a vertical integration approach, exploiting a set of "gold-standard" genes in order to quantify technical artifacts. Additionally, it provides a probabilistic decision rule to identify (i) key drivers of heterogeneity within a population of cells and (ii) changes in gene expression patterns between multiple populations (e.g. experimental conditions or cell types). More recently, we extended BASiCS to experimental designs where gold-standard genes are not available using a horizontal integration framework, where technical variation is quantified through the borrowing of information from observations across multiple groups of samples (e.g. sequencing batches that are not confounded with the biological effect of interest). Control experiments validate our method's performance and a case study suggests that novel biological insights can be revealed.
Our method is implemented in R and available at https://github.com/catavallejos/BASiCS.
[1] Vallejos, Marioni and Richardson (2015) PLoS Computational Biology
[2] Vallejos, Richardson and Marioni (2016) Genome Biology
This seminar is a part of Maxwell Institute seminar series.
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