Back to Search
Start Over
Quantitative assessment of cell population diversity in single-cell landscapes.
- Source :
-
PLoS biology [PLoS Biol] 2018 Oct 22; Vol. 16 (10), pp. e2006687. Date of Electronic Publication: 2018 Oct 22 (Print Publication: 2018). - Publication Year :
- 2018
-
Abstract
- Single-cell RNA sequencing (scRNA-seq) has become a powerful tool for the systematic investigation of cellular diversity. As a number of computational tools have been developed to identify and visualize cell populations within a single scRNA-seq dataset, there is a need for methods to quantitatively and statistically define proportional shifts in cell population structures across datasets, such as expansion or shrinkage or emergence or disappearance of cell populations. Here we present sc-UniFrac, a framework to statistically quantify compositional diversity in cell populations between single-cell transcriptome landscapes. sc-UniFrac enables sensitive and robust quantification in simulated and experimental datasets in terms of both population identity and quantity. We have demonstrated the utility of sc-UniFrac in multiple applications, including assessment of biological and technical replicates, classification of tissue phenotypes and regional specification, identification and definition of altered cell infiltrates in tumorigenesis, and benchmarking batch-correction tools. sc-UniFrac provides a framework for quantifying diversity or alterations in cell populations across conditions and has broad utility for gaining insight into tissue-level perturbations at the single-cell resolution.<br />Competing Interests: The authors have declared that no competing interests exist.
- Subjects :
- Animals
Brain cytology
Brain metabolism
CD4-Positive T-Lymphocytes cytology
CD4-Positive T-Lymphocytes metabolism
CD8-Positive T-Lymphocytes cytology
CD8-Positive T-Lymphocytes metabolism
Cluster Analysis
Computer Simulation
Databases, Nucleic Acid
Gene Expression Profiling statistics & numerical data
Humans
Intestinal Mucosa cytology
Intestinal Mucosa metabolism
Mice
Mice, Inbred C57BL
Models, Biological
Neoplasms, Experimental genetics
Neoplasms, Experimental pathology
Oligodendroglia cytology
Oligodendroglia metabolism
Sequence Analysis, RNA statistics & numerical data
Single-Cell Analysis statistics & numerical data
Software
Workflow
Gene Expression Profiling methods
Sequence Analysis, RNA methods
Single-Cell Analysis methods
Subjects
Details
- Language :
- English
- ISSN :
- 1545-7885
- Volume :
- 16
- Issue :
- 10
- Database :
- MEDLINE
- Journal :
- PLoS biology
- Publication Type :
- Academic Journal
- Accession number :
- 30346945
- Full Text :
- https://doi.org/10.1371/journal.pbio.2006687