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Updated: Feb 19, 2026

Single-Cell Factor Localization on Chromatin using Ultra-Low Input Cleavage Under Targets and Release using Nuclease

Single-Cell Factor Localization on Chromatin using Ultra-Low Input Cleavage Under Targets and Release using Nuclease

Published on: February 1, 2022

f-scLVM: scalable and versatile factor analysis for single-cell RNA-seq.

Florian Buettner^1,2, Naruemon Pratanwanich³, Davis J McCarthy^3,4

¹European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. fbuettner.phys@gmaill.com.

|November 9, 2017

Summary

Factor analysis using factorial single-cell latent variable models (f-scLVM) helps interpret gene expression heterogeneity. This method identifies novel cell subpopulations by decomposing complex single-cell RNA-sequencing data.

Keywords:

Gene set annotations Single-cell RNA-seq Sparse factor analysis

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Last Updated: Feb 19, 2026

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Published on: February 1, 2022

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Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Single-cell RNA-sequencing (scRNA-seq) reveals cellular heterogeneity.
Distinguishing technical from biological variation in scRNA-seq data is challenging.
Interpreting the sources of gene expression variation requires advanced analytical methods.

Purpose of the Study:

To develop a novel method, f-scLVM, for decomposing sources of variation in scRNA-seq data.
To leverage pathway annotations for inferring interpretable biological factors.
To enable robust identification of novel cell subpopulations.

Main Methods:

Factor analysis-based latent variable modeling (f-scLVM).
Joint estimation of factor relevance and gene set annotations.
Inference of factors using pathway annotations and de novo.
Application to multiple independent scRNA-seq datasets.

Main Results:

f-scLVM effectively decomposes scRNA-seq data into interpretable components.
The model successfully refines existing gene set annotations.
Novel biological factors driving heterogeneity were inferred.
Robust decomposition was observed across diverse datasets.

Conclusions:

f-scLVM provides a powerful framework for understanding gene expression heterogeneity in single cells.
The method facilitates the discovery of previously unidentified cell subpopulations.
Integrating pathway information enhances the interpretability of scRNA-seq data analysis.