Statistical significance of cluster membership for unsupervised evaluation of cell identities

Bioinformatics
Neo Christopher Chung

Abstract

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. 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 lin...Continue Reading

References

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Citations

Dec 4, 2020·PloS One·Johan GustafssonJens Nielsen

Related Concepts

Statistical Cluster
Donor Person
Environment
Transcription, Genetic
Patient-Controlled Analgesia
Dorsal
Evaluation
Peripheral Blood
Jurkat Cells
Downstream

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