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Abstract
Single-cell mRNA sequencing can uncover novel cell-to-cell heterogeneity in gene expression levels in seemingly homogeneous populations of cells. However, these experiments are prone to high levels of unexplained technical noise, creating new challenges for identifying genes that show genuine heterogeneous expression within the population of cells under study. BASiCS (Bayesian Analysis of Single-Cell Sequencing data) is an integrated Bayesian hierarchical model where: (i) cell-specific normalisation constants are estimated as part of the model parameters, (ii) technical variability is quantified based on spike-in genes that are artificially introduced to each analysed cell’s lysate and (iii) the total variability of the expression counts is decomposed into technical and biological components. BASiCS also provides an intuitive detection criterion for highly (or lowly) variable genes within the population of cells under study. This is formalised by means of tail posterior probabilities associated to high (or low) biological cell-to-cell variance contributions, quantities that can be easily interpreted by users. We demonstrate our method using gene expression measurements from mouse Embryonic Stem Cells. Cross-validation and meaningful enrichment of gene ontology categories within genes classified as highly (or lowly) variable supports the efficacy of our approach.
Highlights
Current technology allows the analysis of gene expression with high resolution
Even between cells from a seemingly homogeneous tissue sample, there exists substantial heterogeneity in gene expression levels. These differences might correspond to novel subtypes or to transient states linked, for example, to the cell cycle
Unlike most scRNA-seq datasets published to date—where expression counts likely correspond to the number of reads mapped to each gene—Unique Molecular Identifiers (UMI) based datasets are recorded in terms of the number of molecules, producing a meaningful scale for the expression counts
Summary
Current technology allows the analysis of gene expression with high resolution. Instead of measuring average expression levels across a bulk population, scientists can report information at the single-cell level using techniques such as single-cell RNA-sequencing (scRNA-seq) [1]. ScRNA-seq can uncover heterogenous gene expression patterns in seemingly homogeneous populations of cells [2], opening the door to important biological questions that remain otherwise unanswered. Normalisation is a crucial issue in this context Another fundamental problem for interpreting single-cell sequencing is the presence of high levels of unexplained technical noise (unrelated to sequencing depth and other amplification biases) [5]. This creates new challenges for identifying genes that show genuine biological cell-to-cell heterogeneity—beyond that induced by technical variation—and motivates the systematic inclusion of spike-in genes in single-cell experiments. Our analysis of a mouse Embryonic Stem Cells (ESC) suggests that unexplained technical variability can not be completely removed by using UMIs (see Results section) and that an accurate quantification of technical variability still remains important
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