Gene expression distribution deconvolution in single-cell RNA sequencing

Significance We developed deconvolution of single-cell expression distribution (DESCEND), a method to recover cross-cell distribution of the true gene expression level from observed counts in single-cell RNA sequencing, allowing adjustment of known confounding cell-level factors. With the recovered distribution, DESCEND provides reliable estimates of distribution-based measurements, such as the dispersion of true gene expression and the probability that true gene expression is positive. This is important, as with better estimates of these measurements, DESCEND clarifies and improves many downstream analyses including finding differentially expressed genes, identifying cell types, and selecting differentiation markers. Another contribution is that we verified using nine public datasets a simple “Poisson-alpha” noise model for the technical noise of unique molecular identifier-based single-cell RNA-sequencing data, clarifying the current intense debate on this issue. Single-cell RNA sequencing (scRNA-seq) enables the quantification of each gene’s expression distribution across cells, thus allowing the assessment of the dispersion, nonzero fraction, and other aspects of its distribution beyond the mean. These statistical characterizations of the gene expression distribution are critical for understanding expression variation and for selecting marker genes for population heterogeneity. However, scRNA-seq data are noisy, with each cell typically sequenced at low coverage, thus making it difficult to infer properties of the gene expression distribution from raw counts. Based on a reexamination of nine public datasets, we propose a simple technical noise model for scRNA-seq data with unique molecular identifiers (UMI). We develop deconvolution of single-cell expression distribution (DESCEND), a method that deconvolves the true cross-cell gene expression distribution from observed scRNA-seq counts, leading to improved estimates of properties of the distribution such as dispersion and nonzero fraction. DESCEND can adjust for cell-level covariates such as cell size, cell cycle, and batch effects. DESCEND’s noise model and estimation accuracy are further evaluated through comparisons to RNA FISH data, through data splitting and simulations and through its effectiveness in removing known batch effects. We demonstrate how DESCEND can clarify and improve downstream analyses such as finding differentially expressed genes, identifying cell types, and selecting differentiation markers.

[1]  Roger Koenker.  Quantile regression 40 years on , 2017 .

[2]  Timothy K Lee,et al.  Single-cell NF-κB dynamics reveal digital activation and analogue information processing , 2010, Nature.

[3]  M. Schaub,et al.  SC3 - consensus clustering of single-cell RNA-Seq data , 2016, Nature Methods.

[4]  Jong Kyoung Kim,et al.  Corrigendum: Characterizing noise structure in single-cell RNA-seq distinguishes genuine from technical stochastic allelic expression , 2015, Nature Communications.

[5]  J. Marioni,et al.  Inferring the kinetics of stochastic gene expression from single-cell RNA-sequencing data , 2013, Genome Biology.

[6]  A. Raj,et al.  Single mammalian cells compensate for differences in cellular volume and DNA copy number through independent global transcriptional mechanisms. , 2015, Molecular cell.

[7]  Sandhya Prabhakaran,et al.  Dirichlet Process Mixture Model for Correcting Technical Variation in Single-Cell Gene Expression Data , 2016, ICML.

[8]  Sydney M. Shaffer,et al.  Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance , 2017, Nature.

[9]  Shuqiang Li,et al.  CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq , 2016, Genome Biology.

[10]  Grace X. Y. Zheng,et al.  Massively parallel digital transcriptional profiling of single cells , 2016, Nature Communications.

[11]  T. Elston,et al.  Stochasticity in gene expression: from theories to phenotypes , 2005, Nature Reviews Genetics.

[12]  Aleksandra A. Kolodziejczyk,et al.  Accounting for technical noise in single-cell RNA-seq experiments , 2013, Nature Methods.

[13]  Rona S. Gertner,et al.  Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells , 2013, Nature.

[14]  W. Huber,et al.  Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 , 2014, Genome Biology.

[15]  S. Teichmann,et al.  Computational and analytical challenges in single-cell transcriptomics , 2015, Nature Reviews Genetics.

[16]  Evan Z. Macosko,et al.  Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets , 2015, Cell.

[17]  D. Papatsenko,et al.  Quantitative Approaches to Model Pluripotency and Differentiation in Stem Cells , 2013 .

[18]  I. Amit,et al.  Massively Parallel Single-Cell RNA-Seq for Marker-Free Decomposition of Tissues into Cell Types , 2014, Science.

[19]  S. Linnarsson,et al.  Counting absolute numbers of molecules using unique molecular identifiers , 2011, Nature Methods.

[20]  Keegan D. Korthauer,et al.  A statistical approach for identifying differential distributions in single-cell RNA-seq experiments , 2016, Genome Biology.

[21]  A. Oudenaarden,et al.  Validation of noise models for single-cell transcriptomics , 2014, Nature Methods.

[22]  Andrew McDavid,et al.  Data exploration, quality control and testing in single-cell qPCR-based gene expression experiments , 2012, Bioinform..

[23]  D. Tranchina,et al.  Stochastic mRNA Synthesis in Mammalian Cells , 2006, PLoS biology.

[24]  G. Lahav,et al.  We are all individuals: causes and consequences of non-genetic heterogeneity in mammalian cells. , 2011, Current opinion in genetics & development.

[25]  Bradley Efron,et al.  Empirical Bayes deconvolution estimates , 2016 .

[26]  Junhyong Kim,et al.  The promise of single-cell sequencing , 2013, Nature Methods.

[27]  P. Linsley,et al.  MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data , 2015, Genome Biology.

[28]  Sydney M. Shaffer,et al.  Rare Cell Detection by Single-Cell RNA Sequencing as Guided by Single-Molecule RNA FISH. , 2018, Cell systems.

[29]  Jinghua Gu,et al.  Sphinx: modeling transcriptional heterogeneity in single-cell RNA-Seq , 2015 .

[30]  Nancy R. Zhang,et al.  Accounting for technical noise in single-cell RNA sequencing analysis , 2017, bioRxiv.

[31]  David A. Knowles,et al.  Batch effects and the effective design of single-cell gene expression studies , 2016, Scientific Reports.

[32]  Krishna R. Kalari,et al.  Beta-Poisson model for single-cell RNA-seq data analyses , 2016, Bioinform..

[33]  P. Kharchenko,et al.  Bayesian approach to single-cell differential expression analysis , 2014, Nature Methods.

[34]  R. Stewart,et al.  Single-cell RNA-seq reveals novel regulators of human embryonic stem cell differentiation to definitive endoderm , 2016, Genome Biology.

[35]  P. Sorger,et al.  Non-genetic origins of cell-to-cell variability in TRAIL-induced apoptosis , 2009, Nature.

[36]  Nancy R. Zhang,et al.  SCALE: modeling allele-specific gene expression by single-cell RNA sequencing , 2017, Genome Biology.

[37]  Aleksandra A. Kolodziejczyk,et al.  The technology and biology of single-cell RNA sequencing. , 2015, Molecular cell.

[38]  Martin Hemberg,et al.  Discrete distributional differential expression (D3E) - a tool for gene expression analysis of single-cell RNA-seq data , 2015, BMC Bioinformatics.

[39]  Manikandan Narayanan,et al.  Robust Inference of Cell-to-Cell Expression Variations from Single- and K-Cell Profiling , 2016, PLoS Comput. Biol..

[40]  Allon M. Klein,et al.  Droplet Barcoding for Single-Cell Transcriptomics Applied to Embryonic Stem Cells , 2015, Cell.

[41]  Aleksandra A. Kolodziejczyk,et al.  Characterizing noise structure in single-cell RNA-seq distinguishes genuine from technical stochastic allelic expression , 2015, Nature Communications.

[42]  S. Linnarsson,et al.  Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq , 2015, Science.

[43]  Valentine Svensson,et al.  Power Analysis of Single Cell RNA-Sequencing Experiments , 2016, Nature Methods.

[44]  Sandrine Dudoit,et al.  Normalizing single-cell RNA sequencing data: challenges and opportunities , 2017, Nature Methods.

[45]  Fabian J Theis,et al.  Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells , 2015, Nature Biotechnology.

[46]  Rona S. Gertner,et al.  Single cell RNA Seq reveals dynamic paracrine control of cellular variation , 2014, Nature.

[47]  Grace X. Y. Zheng,et al.  Massively parallel digital transcriptional profiling of single cells , 2016, bioRxiv.

[48]  Catalina A. Vallejos,et al.  BASiCS: Bayesian Analysis of Single-Cell Sequencing Data , 2015, PLoS Comput. Biol..