Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model

Single-cell RNA-Seq (scRNA-Seq) profiles gene expression of individual cells. Recent scRNA-Seq datasets have incorporated unique molecular identifiers (UMIs). Using negative controls, we show UMI counts follow multinomial sampling with no zero inflation. Current normalization procedures such as log of counts per million and feature selection by highly variable genes produce false variability in dimension reduction. We propose simple multinomial methods, including generalized principal component analysis (GLM-PCA) for non-normal distributions, and feature selection using deviance. These methods outperform the current practice in a downstream clustering assessment using ground truth datasets.

[1]  H. Hotelling.  Analysis of a complex of statistical variables into principal components. , 1933 .

[2]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[3]  Maurice K. Wong,et al.  Algorithm AS136: A k-means clustering algorithm. , 1979 .

[4]  D. McDonald,et al.  On the poisson approximation to the multinomial distribution , 1980 .

[5]  L. Hubert,et al.  Comparing partitions , 1985 .

[6]  S. Baker.  The Multinomial‐Poisson Transformation , 1994 .

[7]  Sanjoy Dasgupta,et al.  A Generalization of Principal Components Analysis to the Exponential Family , 2001, NIPS.

[8]  G. Mateu-Figueras,et al.  Isometric Logratio Transformations for Compositional Data Analysis , 2003 .

[9]  L. Pachter.  Models for transcript quantification from RNA-Seq , 2011, 1104.3889.

[10]  Daniela M. Witten,et al.  Classification and clustering of sequencing data using a poisson model , 2011, 1202.6201.

[11]  E. Shapiro,et al.  Single-cell sequencing-based technologies will revolutionize whole-organism science , 2013, Nature Reviews Genetics.

[12]  David M. Blei,et al.  Scalable Recommendation with Poisson Factorization , 2013, ArXiv.

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

[14]  Åsa K. Björklund,et al.  Smart-seq2 for sensitive full-length transcriptome profiling in single cells , 2013, Nature Methods.

[15]  Gioele La Manno,et al.  Quantitative single-cell RNA-seq with unique molecular identifiers , 2013, Nature Methods.

[16]  Cole Trapnell,et al.  The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells , 2014, Nature Biotechnology.

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

[18]  Haesun Park,et al.  Algorithms for nonnegative matrix and tensor factorizations: a unified view based on block coordinate descent framework , 2014, J. Glob. Optim..

[19]  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.

[20]  E. Pierson,et al.  ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis , 2015, Genome Biology.

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

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

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

[24]  Matt Taddy,et al.  Distributed multinomial regression , 2013, 1311.6139.

[25]  A. Agresti.  Foundations of Linear and Generalized Linear Models , 2015 .

[26]  Andrew J. Landgraf,et al.  Generalized Principal Component Analysis: Dimensionality Reduction through the Projection of Natural Parameters , 2015 .

[27]  M. Cugmas,et al.  On comparing partitions , 2015 .

[28]  J. Marioni,et al.  Pooling across cells to normalize single-cell RNA sequencing data with many zero counts , 2016, Genome Biology.

[29]  Cole Trapnell,et al.  Single-cell transcriptome sequencing: recent advances and remaining challenges , 2016, F1000Research.

[30]  Davis J. McCarthy,et al.  A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor , 2016, F1000Research.

[31]  Mauro J. Muraro,et al.  A Single-Cell Transcriptome Atlas of the Human Pancreas , 2016, Cell systems.

[32]  Andrew D. Ellington,et al.  Synthetic evolutionary origin of a proofreading reverse transcriptase , 2016, Science.

[33]  Surojit Biswas.  The latent logarithm , 2016, 1605.06064.

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

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

[36]  Florian Wagner,et al.  K-nearest neighbor smoothing for high-throughput single-cell RNA-Seq data , 2017, bioRxiv.

[37]  Aaron T. L. Lun,et al.  Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R , 2017, Bioinform..

[38]  S. Dudoit,et al.  A general and flexible method for signal extraction from single-cell RNA-seq data , 2018, Nature Communications.

[39]  Andrew J. Hill,et al.  Single-cell mRNA quantification and differential analysis with Census , 2017, Nature Methods.

[40]  Yarden Katz,et al.  A single-cell survey of the small intestinal epithelium , 2017, Nature.

[41]  Joshua W. K. Ho,et al.  CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data , 2016, Genome Biology.

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

[43]  D. Warton.  Why you cannot transform your way out of trouble for small counts , 2018, Biometrics.

[44]  Charlotte Soneson,et al.  A systematic performance evaluation of clustering methods for single-cell RNA-seq data , 2018, F1000Research.

[45]  S. Teichmann,et al.  Exponential scaling of single-cell RNA-seq in the past decade , 2017, Nature Protocols.

[46]  M. Robinson,et al.  A systematic performance evaluation of clustering methods for single-cell RNA-seq data. , 2018, F1000Research.

[47]  Leland McInnes,et al.  UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction , 2018, ArXiv.

[48]  Saumyadipta Pyne,et al.  A brief review of single-cell transcriptomic technologies. , 2018, Briefings in functional genomics.

[49]  Paul Hoffman,et al.  Integrating single-cell transcriptomic data across different conditions, technologies, and species , 2018, Nature Biotechnology.

[50]  Gen Li,et al.  A general framework for association analysis of heterogeneous data , 2017, The Annals of Applied Statistics.

[51]  Charlotte Soneson,et al.  Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications , 2018, Genome Biology.

[52]  Barbara E. Engelhardt,et al.  A robust nonlinear low-dimensional manifold for single cell RNA-seq data , 2018, BMC Bioinformatics.

[53]  Charlotte Soneson,et al.  Bias, robustness and scalability in single-cell differential expression analysis , 2018, Nature Methods.

[54]  M. Hemberg,et al.  Identifying cell populations with scRNASeq. , 2017, Molecular aspects of medicine.

[55]  Aaron Lun,et al.  Overcoming systematic errors caused by log-transformation of normalized single-cell RNA sequencing data , 2018, bioRxiv.

[56]  R. Irizarry,et al.  Missing data and technical variability in single‐cell RNA‐sequencing experiments , 2018, Biostatistics.

[57]  Sayan Mukherjee,et al.  Naught all zeros in sequence count data are the same , 2018, bioRxiv.

[58]  Michael I. Jordan,et al.  Deep Generative Modeling for Single-cell Transcriptomics , 2018, Nature Methods.

[59]  S. Teichmann,et al.  SpatialDE: identification of spatially variable genes , 2018, Nature Methods.

[60]  Martin Hemberg,et al.  M3Drop: dropout-based feature selection for scRNASeq , 2018, Bioinform..

[61]  F. William Townes,et al.  Generalized Principal Component Analysis , 2019, ArXiv.

[62]  R. Satija,et al.  Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression , 2019, Genome Biology.

[63]  Valentine Svensson,et al.  Droplet scRNA-seq is not zero-inflated , 2019, Nature Biotechnology.

[64]  Barbara Di Camillo,et al.  How to design a single-cell RNA-sequencing experiment: pitfalls, challenges and perspectives , 2019, Briefings Bioinform..