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scLVM specifications


Unique identifier OMICS_07577
Name scLVM
Alternative name single-cell Latent Variable Model
Software type Application/Script
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Programming languages Python, R
Computer skills Advanced
Stability Beta
Maintained Yes


No version available



  • person_outline scLVM
  • person_outline John Marioni

Publication for single-cell Latent Variable Model

scLVM citations


Single cell RNA sequencing resolves self antigen expression during mTEC development

Sci Rep
PMCID: 5766627
PMID: 29330484
DOI: 10.1038/s41598-017-19100-4

[…] ementary Figure ). All but six mTECs were in G1/G0 phase, suggesting relatively modest cell cycle effects over gene expression in this population. Nonetheless, we investigated this further by running scLVM package to evaluate and regress out any cell cycle related biases. We observed that when performing PCA on the scLVM-corrected data, PC1, PC2 and PC3 all correlated with the cell size to some ex […]


Single cell RNA seq reveals hidden transcriptional variation in malaria parasites

PMCID: 5871331
PMID: 29580379
DOI: 10.7554/eLife.33105.044

[…] P. berghei trophozoite and schizont single cells were processed using scLVM v0.99.2 (). Counts were normalised by size factors. Technical noise was estimated using a log fit and 1485 variable genes were called using getVariableGenes with default parameters. To fit the l […]


Using single cell multiple omics approaches to resolve tumor heterogeneity

PMCID: 5746494
PMID: 29285690
DOI: 10.1186/s40169-017-0177-y

[…] noise []. For example, Monocle2 is an unsupervised algorithm designed to analyze the heterogeneity among cells and reconstruct the micro-evolution timeline from scRNA-seq data []. Other tools such as scLVM [], PseudoGP [], and SPADE [] have provided various solutions to analyze heterogeneity with scRNA-seq data computationally. With the scRNA-seq analysis toolbox expanding rapidly, graphical user […]


Single cell RNA sequencing uncovers transcriptional states and fate decisions in haematopoiesis

Nat Commun
PMCID: 5725498
PMID: 29229905
DOI: 10.1038/s41467-017-02305-6

[…] To distinguish biological variability from the technical noise in our single-cell experiments, we inferred the most highly variable genes using ERCCs as spike-in in all 1422 blood cells. We used the scLVM R package (version 0.99.2) to identify the 1845 most highly variable genes (Supplementary Fig. ).Principal component analysis (pcaMethods (version 1.64.0)), independent component analysis (FastI […]


Telomere heterogeneity linked to metabolism and pluripotency state revealed by simultaneous analysis of telomere length and RNA seq in the same human embryonic stem cell

BMC Biol
PMCID: 5721592
PMID: 29216888
DOI: 10.1186/s12915-017-0453-8

[…] 1/OCT4 (Additional file : Figure S4), further validating the single cell RNA-seq data. Cell cycle stage may have influenced the heterogeneity of gene expression []. We employed the prediction method (single-cell latent variable model, scLVM) to classify cells into cell cycle phases based on gene expression data. Only 11.6% of variation was attributable to the cell cycle phases, and cell cycle stag […]


APE1/Ref‐1 knockdown in pancreatic ductal adenocarcinoma – characterizing gene expression changes and identifying novel pathways using single‐cell RNA sequencing

Mol Oncol
PMCID: 5709621
PMID: 28922540
DOI: 10.1002/1878-0261.12138

[…] 1 and SCR cells were split across three batches, with one batch containing siAPE1 and two batches containing SCR cells (SCR1 and SCR2). Differences between cell batches were corrected by applying the scLVM r package (Buettner et al., ). In conjunction with scLVM, the Biomart r package (Durinck et al., ) was used to obtain a list of cell cycle‐annotated genes. Specifically, the Gene Ontology (GO) t […]


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scLVM institution(s)
Helmholtz Zentrum München-German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany; European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK; Wellcome Trust Sanger Institute, Hinxton, UK; Department of Mathematics, Technische Universität München, Munich, Germany

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