ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis

Single-cell RNA-seq data allows insight into normal cellular function and various disease states through molecular characterization of gene expression on the single cell level. Dimensionality reduction of such high-dimensional data sets is essential for visualization and analysis, but single-cell RN...

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Bibliographic Details
Published in:Genome Biology Vol. 16; no. 1; p. 241
Main Authors: Pierson, Emma, Yau, Christopher
Format: Journal Article
Language:English
Published: London BioMed Central 02.11.2015
Springer Nature B.V
Subjects:
Algorithms
Animal Genetics and Genomics
Bioinformatics
Biomedical and Life Sciences
Data processing
Datasets
Earthquakes
Evolutionary Biology
Gene expression
Gene Expression Profiling - methods
genome
Human Genetics
Life Sciences
Linear algebra
Microbial Genetics and Genomics
Models, Statistical
Plant Genetics and Genomics
Principal Component Analysis
Ribonucleic acid
RNA
School dropout programs
School dropouts
sequence analysis
Sequence Analysis, RNA
Single-Cell Analysis
Software
Posterior Predictive Distribution
Principal Component Analysis
Standard Principal Component Analysis
Gaussian Process Latent Variable Model
Probabilistic Principal Component Analysis
ISSN:1474-760X, 1474-7596, 1474-760X
Online Access:Get full text
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Summary:Single-cell RNA-seq data allows insight into normal cellular function and various disease states through molecular characterization of gene expression on the single cell level. Dimensionality reduction of such high-dimensional data sets is essential for visualization and analysis, but single-cell RNA-seq data are challenging for classical dimensionality-reduction methods because of the prevalence of dropout events, which lead to zero-inflated data. Here, we develop a dimensionality-reduction method, (Z)ero (I)nflated (F)actor (A)nalysis (ZIFA), which explicitly models the dropout characteristics, and show that it improves modeling accuracy on simulated and biological data sets.
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ISSN:1474-760X
1474-7596
1474-760X
DOI:10.1186/s13059-015-0805-z