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Statistical significance of cluster membership for unsupervised evaluation of cell identities.

ABSTRACT: MOTIVATION:Single-cell RNA-sequencing (scRNA-seq) allows us to dissect transcriptional heterogeneity arising from cellular types, spatio-temporal contexts and environmental stimuli. Transcriptional heterogeneity may reflect phenotypes and molecular signatures that are often unmeasured or unknown a priori. Cell identities of samples derived from heterogeneous subpopulations are then determined by clustering of scRNA-seq data. These cell identities are used in downstream analyses. How can we examine if cell identities are accurately inferred? Unlike external measurements or labels for single cells, using clustering-based cell identities result in spurious signals and false discoveries. RESULTS:We introduce non-parametric methods to evaluate cell identities by testing cluster memberships in an unsupervised manner. Diverse simulation studies demonstrate accuracy of the jackstraw test for cluster membership. We propose a posterior probability that a cell should be included in that clustering-based subpopulation. Posterior inclusion probabilities (PIPs) for cluster memberships can be used to select and visualize samples relevant to subpopulations. The proposed methods are applied on three scRNA-seq datasets. First, a mixture of Jurkat and 293T cell lines provides two distinct cellular populations. Second, Cell Hashing yields cell identities corresponding to eight donors which are independently analyzed by the jackstraw. Third, peripheral blood mononuclear cells are used to explore heterogeneous immune populations. The proposed P-values and PIPs lead to probabilistic feature selection of single cells that can be visualized using principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE) and others. By learning uncertainty in clustering high-dimensional data, the proposed methods enable unsupervised evaluation of cluster membership. AVAILABILITY AND IMPLEMENTATION:https://cran.r-project.org/package=jackstraw. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

SUBMITTER: Chung NC

PROVIDER: S-EPMC7214036 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Statistical significance of cluster membership for unsupervised evaluation of cell identities.

Chung Neo Christopher NC

Bioinformatics (Oxford, England) 20200501 10

<h4>Motivation</h4>Single-cell RNA-sequencing (scRNA-seq) allows us to dissect transcriptional heterogeneity arising from cellular types, spatio-temporal contexts and environmental stimuli. Transcriptional heterogeneity may reflect phenotypes and molecular signatures that are often unmeasured or unknown a priori. Cell identities of samples derived from heterogeneous subpopulations are then determined by clustering of scRNA-seq data. These cell identities are used in downstream analyses. How can ...[more]

PMID: 32142108

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Project description:ImportanceDisparities in medical student membership in Alpha Omega Alpha (AOA) are well documented. Less is known about Gold Humanism Honor Society (GHHS) membership and it remains unknown how the intersection of different identities is associated with membership in these honor societies.ObjectiveTo examine the association between honor society membership and medical student race and ethnicity, sex, sexual orientation, socioeconomic status, and intersection of identities.Design, setting, and participantsThis cross-sectional study analyzed data from Association of American Medical Colleges data collection instruments. The study included all students who graduated from Liaison Committee on Medical Education-accredited US medical schools from 2016 to 2019 and completed the Graduation Questionnaire. Data analysis was conducted from January 12 to July 12, 2022.Main outcomes and measuresLikelihood of AOA and GHHS membership by student race and ethnicity, sex, sexual orientation, childhood family income, and intersection of identities.ResultsThe sample of 50 384 individuals comprised 82 (0.2%) American Indian or Alaska Native, 10 601 (21.0%) Asian, 2464 (4.9%) Black, 3291 (6.5%) Hispanic, 25 (0.1%) Native Hawaiian or Pacific Islander, 30 610 (60.8%) White, 2476 (4.9%) multiracial students, and 834 (1.7%) students of other races or ethnicities. Sex and sexual orientation included 25 672 (51.0%) men and 3078 (6.1%) lesbian, gay, and bisexual (LGB). Childhood family income comprised 31 758 (60.0%) individuals with $75 000 per year or greater, 8160 (16.2%) with $50 000 to $74 999 per year, 6864 (13.6%) with $25 000 to $49 999 per year, and 3612 (7.2%) with less than $25 000 per year. The sample included 7303 (14.5%) AOA members only, 4925 (9.8%) GHHS members only, and 2384 (4.7%) members of both societies. In AOA, American Indian or Alaska Native (OR, 0.49; 95% CI, 0.25-0.96), Asian (OR, 0.49; 95% CI, 0.45-0.53), Black (OR, 0.25; 95% CI, 0.20-0.30), Hispanic (OR, 0.53; 95% CI, 0.47-0.59), multiracial (OR, 0.69; 95% CI, 0.62-0.77), and other race and ethnicity (OR, 0.73; 95% CI, 0.60-0.88) were underrepresented compared with White students; LGB students (OR, 0.75; 95% CI, 0.67-0.83) were underrepresented compared with heterosexual students; and childhood family income $50 000 to $74 999 (OR, 0.81; 95% CI, 0.75-0.86), $25 000 to $49 999 (OR, 0.68; 95% CI, 0.62-0.74), and less than $25 000 (OR, 0.60; 95% CI, 0.53-0.69) were underrepresented compared with greater than or equal to $75 000. In GHHS, Asian students (OR, 0.80; 95% CI, 0.73-0.87) were underrepresented compared with White students, female students (OR, 1.55; 95% CI, 1.45-1.65) were overrepresented compared with male students, LGB students (OR, 1.36; 95% CI, 1.23-1.51) were overrepresented compared with heterosexual students, and students with childhood family income $25 000 to $49 999 (OR, 0.85; 95% CI, 0.78-0.94) and less than $25 000 (OR, 0.75; 95% CI, 0.66-0.86) were underrepresented compared with those with greater than or equal to $75 000. Likelihood of AOA, but not GHHS, membership decreased as number of marginalized identities increased.Conclusions and relevanceIn this cross-sectional study of US medical students, membership disparities were noted in both AOA and GHHS. However, differences in GHHS existed across fewer identities, sometimes favored the marginalized group, and were not cumulative.

Project description:BackgroundCausal graphs are an increasingly popular tool for the analysis of biological datasets. In particular, signed causal graphs--directed graphs whose edges additionally have a sign denoting upregulation or downregulation--can be used to model regulatory networks within a cell. Such models allow prediction of downstream effects of regulation of biological entities; conversely, they also enable inference of causative agents behind observed expression changes. However, due to their complex nature, signed causal graph models present special challenges with respect to assessing statistical significance. In this paper we frame and solve two fundamental computational problems that arise in practice when computing appropriate null distributions for hypothesis testing.ResultsFirst, we show how to compute a p-value for agreement between observed and model-predicted classifications of gene transcripts as upregulated, downregulated, or neither. Specifically, how likely are the classifications to agree to the same extent under the null distribution of the observed classification being randomized? This problem, which we call "Ternary Dot Product Distribution" owing to its mathematical form, can be viewed as a generalization of Fisher's exact test to ternary variables. We present two computationally efficient algorithms for computing the Ternary Dot Product Distribution and investigate its combinatorial structure analytically and numerically to establish computational complexity bounds.Second, we develop an algorithm for efficiently performing random sampling of causal graphs. This enables p-value computation under a different, equally important null distribution obtained by randomizing the graph topology but keeping fixed its basic structure: connectedness and the positive and negative in- and out-degrees of each vertex. We provide an algorithm for sampling a graph from this distribution uniformly at random. We also highlight theoretical challenges unique to signed causal graphs; previous work on graph randomization has studied undirected graphs and directed but unsigned graphs.ConclusionWe present algorithmic solutions to two statistical significance questions necessary to apply the causal graph methodology, a powerful tool for biological network analysis. The algorithms we present are both fast and provably correct. Our work may be of independent interest in non-biological contexts as well, as it generalizes mathematical results that have been studied extensively in other fields.

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Statistical significance of cluster membership for unsupervised evaluation of cell identities.

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Statistical significance of cluster membership for unsupervised evaluation of cell identities.

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