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f-scLVM: scalable and versatile factor analysis for single-cell RNA-seq
- Source :
- Genome Biology, Vol 18, Iss 1, Pp 1-13 (2017), Genome Biol. 18:212 (2017), Genome Biology
- Publication Year :
- 2018
- Publisher :
- Springer Science and Business Media LLC, 2018.
-
Abstract
- Single-cell RNA-sequencing (scRNA-seq) allows studying heterogeneity in gene expression in large cell populations. Such heterogeneity can arise due to technical or biological factors, making decomposing sources of variation difficult. We here describe f-scLVM (factorial single-cell latent variable model), a method based on factor analysis that uses pathway annotations to guide the inference of interpretable factors underpinning the heterogeneity. Our model jointly estimates the relevance of individual factors, refines gene set annotations, and infers factors without annotation. In applications to multiple scRNA-seq datasets, we find that f-scLVM robustly decomposes scRNA-seq datasets into interpretable components, thereby facilitating the identification of novel subpopulations. Electronic supplementary material The online version of this article (doi:10.1186/s13059-017-1334-8) contains supplementary material, which is available to authorized users.
- Subjects :
- 0301 basic medicine
lcsh:QH426-470
Single-cell Rna-seq
Sparse Factor Analysis
Gene Set Annotations
Inference
Method
RNA-Seq
Computational biology
Biology
Set (abstract data type)
03 medical and health sciences
Annotation
Mice
Single-cell analysis
Animals
Relevance (information retrieval)
Computer Simulation
Latent variable model
lcsh:QH301-705.5
Single-cell RNA-seq
Genetics
Neurons
Sequence Analysis, RNA
Sparse factor analysis
Reproducibility of Results
Mouse Embryonic Stem Cells
Models, Theoretical
Identification (information)
lcsh:Genetics
030104 developmental biology
lcsh:Biology (General)
Databases as Topic
Gene Expression Regulation
Single-Cell Analysis
Gene set annotations
Factor Analysis, Statistical
Software
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Genome Biology, Vol 18, Iss 1, Pp 1-13 (2017), Genome Biol. 18:212 (2017), Genome Biology
- Accession number :
- edsair.doi.dedup.....4217acb80d20ece0817595318619a827
- Full Text :
- https://doi.org/10.17863/cam.21470