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Cited by 273Single-cell and spatial transcriptomicsGene expression and cancer classificationGenomics and Phylogenetic Studies

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A statistical approach for identifying differential distributions in single-cell RNA-seq experiments

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Korthauer, Keegan D., Chu, Li-Fang, Newton, Michael A., Li, Yuan, Thomson, James, Stewart, Ron, Kendziorski, Christina·2016·Genome Biology
DOI 10.1186/s13059-016-1077-yPMC PMC5080738

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Abstract

Abstract The ability to quantify cellular heterogeneity is a major advantage of single-cell technologies. Although understanding such heterogeneity is of primary interest in a number of studies, for convenience, statistical methods often treat cellular heterogeneity as a nuisance factor. We present a novel method to characterize differences in expression in the presence of distinct expression states within and among biological conditions. Using simulated and case study data, we demonstrate that the modeling framework is able to detect differential expression patterns of interest under a wide range of settings. Compared to existing approaches, scDD has higher power to detect subtle differences in gene expression distributions that are more complex than a mean shift, and is able to characterize those differences. The freely available R package scDD implements the approach.

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Fig. 1 Schematic of the presence of two cell states within a cell population that can lead to bimodal expression distributions. a Time series of the underlying expression state of gene X in a population of unsynchronized single cells, which switches back and forth between a low and high state with means μ 1 and μ 2, respectively. The color of cells at each time point corresponds to the underlying expression state. b Population of individual cells shaded by expression state of gene X at a snapshot in time. c Histogram of the observed expression level of gene X for the cell population in (b)
Fig. 1 Schematic of the presence of two cell states within a cell population that can lead to bimodal expression distributions. a Time series of the underlying expression state of gene X in a population of unsynchronized single cells, which switches back and forth between a low and high state with means μ 1 and μ 2, respectively. The color of cells at each time point corresponds to the underlying expression state. b Population of individual cells shaded by expression state of gene X at a snapshot in time. c Histogram of the observed expression level of gene X for the cell population in (b)CC BY · PubMed Central
Fig. 2 Comparison of modality in bulk versus single cells. Bar plot of the proportion of genes (or transcripts) in each dataset where the log-transformed nonzero expression measurements are best fit by a 1, 2, or 3 + mode normal mixture model (where 3+ denotes 3 or more). Modality is determined using a Bayesian information selection criterion with filtering (see “Partition estimation”). Red shades denote bulk RNA-seq datasets, and blue shades denote single-cell datasets. The number following each dataset label indicates the number of samples present (e.g., GE.50 is a bulk dataset with 50 samples). Datasets GE.50, GE.75, and GE.100 are constructed by randomly sampling 50, 75, and 100 samples from GEUVADIS [56]. Dataset LC consists of 77 normal samples from the TCGA lung adenocarcinoma study [57]. For details of the single-cell datasets, see “Methods”
Fig. 2 Comparison of modality in bulk versus single cells. Bar plot of the proportion of genes (or transcripts) in each dataset where the log-transformed nonzero expression measurements are best fit by a 1, 2, or 3 + mode normal mixture model (where 3+ denotes 3 or more). Modality is determined using a Bayesian information selection criterion with filtering (see “Partition estimation”). Red shades denote bulk RNA-seq datasets, and blue shades denote single-cell datasets. The number following each dataset label indicates the number of samples present (e.g., GE.50 is a bulk dataset with 50 samples). Datasets GE.50, GE.75, and GE.100 are constructed by randomly sampling 50, 75, and 100 samples from GEUVADIS [56]. Dataset LC consists of 77 normal samples from the TCGA lung adenocarcinoma study [57]. For details of the single-cell datasets, see “Methods”CC BY · PubMed Central
Fig. 3 Diagram of plausible differential distribution patterns (smoothed density histograms), including a traditional differential expression (DE), b differential proportion of cells within each component (DP), c differential modality (DM), and d both differential modality and different component means within each condition (DB). DB both differential modality and different component means, DE differential expression, DM differential modality, DP differential proportion
Fig. 3 Diagram of plausible differential distribution patterns (smoothed density histograms), including a traditional differential expression (DE), b differential proportion of cells within each component (DP), c differential modality (DM), and d both differential modality and different component means within each condition (DB). DB both differential modality and different component means, DE differential expression, DM differential modality, DP differential proportionCC BY · PubMed Central
Fig. 4 Relationship of cell types used in hESC case study. H1 and H9 are undifferentiated hESC lines. NPC (neuronal progenitor cells) and DEC (definitive endoderm cells) are differentiated cell types derived from H1. DEC definitive endoderm cell, NPC neuronal progenitor cell
Fig. 4 Relationship of cell types used in hESC case study. H1 and H9 are undifferentiated hESC lines. NPC (neuronal progenitor cells) and DEC (definitive endoderm cells) are differentiated cell types derived from H1. DEC definitive endoderm cell, NPC neuronal progenitor cellCC BY · PubMed Central
Fig. 5 Violin plots (smoothed non-parametric kernel density estimates) for Differentially Distributed genes identified between H1 and DEC. Individual observations are displayed with jitter. Within a condition, points with the same shape are predicted to belong to the same component. a scDD-exclusive genes: representative genes from each category (DZ, DP, DM, and DB) that are not detected by MAST or SCDE. Selected genes are top-ranked by permutation p value in each category (DP, DM, and DB) or had a significant χ 2 test for a difference in the proportion of zeroes (DZ). b Cell-cycle genes: DD genes involved in cell-cycle regulation (not detected by MAST or SCDE). c Pluripotency genes: DD genes involved in pluripotency regulation (not identified by MAST or SCDE). DB both differential modality and different component means, DD differential distribution, DEC definitive endoderm cell, DM differential modality, DP differential proportion, DZ differential zeroes
Fig. 5 Violin plots (smoothed non-parametric kernel density estimates) for Differentially Distributed genes identified between H1 and DEC. Individual observations are displayed with jitter. Within a condition, points with the same shape are predicted to belong to the same component. a scDD-exclusive genes: representative genes from each category (DZ, DP, DM, and DB) that are not detected by MAST or SCDE. Selected genes are top-ranked by permutation p value in each category (DP, DM, and DB) or had a significant χ 2 test for a difference in the proportion of zeroes (DZ). b Cell-cycle genes: DD genes involved in cell-cycle regulation (not detected by MAST or SCDE). c Pluripotency genes: DD genes involved in pluripotency regulation (not identified by MAST or SCDE). DB both differential modality and different component means, DD differential distribution, DEC definitive endoderm cell, DM differential modality, DP differential proportion, DZ differential zeroesCC BY · PubMed Central

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Metadata

Authors
Korthauer, K. D., Chu, L.-F., Newton, M. A., Li, Y., Thomson, J., Stewart, R., & Kendziorski, C.
Affiliations
  • Harvard University (US)
  • University of Wisconsin–Madison (US)
  • University of Wisconsin System (US)
ORCID
Venue
Genome Biology
Publisher
Springer Science and Business Media LLC
Published
2016-10-25
Volume
17
Issue
1
Pages
222-222
ISSN
  • 1465-6906 (ISSN-L)
  • 1474-760X
DOI
10.1186/s13059-016-1077-y
PMC
PMC5080738
OpenAlex
W2950541340
Cited by
273
References
73
Citations by year
2026: 11 · 2025: 15 · 2024: 26 · 2023: 31 · 2022: 36 · 2021: 56 · 2020: 29 · 2019: 21 · 2018: 24 · 2017: 18 · 2016: 5
FWCI
11.78
Type
journal article
Language
en
Open access
Licence
Locations
  • Genome biology (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
Funders
  • National Institute of General Medical Sciences (GM102756)
  • National Institute of Allergy and Infectious Diseases (U54AI117924)
  • National Institutes of Health (4UH3TR000506-03)
  • National Heart, Lung, and Blood Institute (5U01HL099773-06)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Keywords
differential gene expression, single-cell RNA sequencing, gene expression distribution, cellular heterogeneity
MeSH terms
Algorithms, Humans, RNA, Software, Sequence Analysis, RNA, Computational Biology, Gene Expression Profiling, Single-Cell Analysis, High-Throughput Nucleotide Sequencing

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