Valid Post-clustering Differential Analysis for Single-Cell RNA-Seq.

Single-cell computational pipelines involve two critical steps: organizing cells (clustering) and identifying the markers driving this organization (differential expression analysis). State-of-the-art pipelines perform differential analysis after clustering on the same dataset. We observe that because clustering "forces" separation, reusing the same dataset generates artificially low p values and hence false discoveries. We introduce a valid post-clustering differential analysis framework, which corrects for this problem. We provide software at https://github.com/jessemzhang/tn_test.

[1]  Sean C. Bendall,et al.  Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis , 2015, Cell.

[2]  Ben S. Wittner,et al.  Single-Cell RNA Sequencing Identifies Extracellular Matrix Gene Expression by Pancreatic Circulating Tumor Cells , 2014, Cell reports.

[3]  David Tse,et al.  An interpretable framework for clustering single-cell RNA-Seq datasets , 2017, BMC Bioinformatics.

[4]  Fabian J Theis,et al.  SCANPY: large-scale single-cell gene expression data analysis , 2018, Genome Biology.

[5]  Dennis L. Sun,et al.  Optimal Inference After Model Selection , 2014, 1410.2597.

[6]  E. Lehmann.  Testing Statistical Hypotheses , 1960 .

[7]  Lior Pachter,et al.  Fast and accurate single-cell RNA-seq analysis by clustering of transcript-compatibility counts , 2016, Genome Biology.

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

[9]  Howard Y. Chang,et al.  Single-cell chromatin accessibility reveals principles of regulatory variation , 2015, Nature.

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

[11]  F. D’Acquisto,et al.  Annexin-1 modulates T-cell activation and differentiation. , 2007, Blood.

[12]  Maria Kasper,et al.  Single-Cell Transcriptomics Reveals that Differentiation and Spatial Signatures Shape Epidermal and Hair Follicle Heterogeneity , 2016, Cell systems.

[13]  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.

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

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

[16]  Jonathan A. Bernstein,et al.  Assembly of functionally integrated human forebrain spheroids , 2017, Nature.

[17]  Aleksandra A. Kolodziejczyk,et al.  Single Cell RNA-Sequencing of Pluripotent States Unlocks Modular Transcriptional Variation , 2015, Cell stem cell.

[18]  Tsippi Iny Stein,et al.  The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses , 2016, Current protocols in bioinformatics.

[19]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[20]  Aviv Regev,et al.  Massively-parallel single nucleus RNA-seq with DroNc-seq , 2017, Nature Methods.

[21]  Vitor R. C. Aguiar,et al.  Mapping Bias Overestimates Reference Allele Frequencies at the HLA Genes in the 1000 Genomes Project Phase I Data , 2014, G3: Genes, Genomes, Genetics.

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

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

[24]  Chen Xu,et al.  Identification of cell types from single-cell transcriptomes using a novel clustering method , 2015, Bioinform..

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

[26]  Alex A. Pollen,et al.  Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex , 2014, Nature Biotechnology.

[27]  S. Linnarsson,et al.  Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing , 2014, Nature Neuroscience.

[28]  A. Buja,et al.  Valid post-selection inference , 2013, 1306.1059.

[29]  Ruiqiang Li,et al.  Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells , 2013, Nature Structural &Molecular Biology.

[30]  F. Biase,et al.  Cell fate inclination within 2-cell and 4-cell mouse embryos revealed by single-cell RNA sequencing , 2014, Genome research.

[31]  N. Neff,et al.  Reconstructing lineage hierarchies of the distal lung epithelium using single cell RNA-seq , 2014, Nature.

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

[33]  Shawn M. Gillespie,et al.  Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma , 2014, Science.

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

[35]  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.

[36]  J. Ioannidis.  Why Most Published Research Findings Are False , 2019, CHANCE.

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

[38]  R. Sandberg,et al.  Single-Cell RNA-Seq Reveals Dynamic, Random Monoallelic Gene Expression in Mammalian Cells , 2014, Science.

[39]  Student,et al.  THE PROBABLE ERROR OF A MEAN , 1908 .