cellID-package: Multiple Correspondence Analysis on Single Cell for Joint...

In cbl-imagine/CellID: Unbiased Extraction of Single Cell Identity using Multiple Correspondence Analysis

Description

CellID take as input a matrix of expression level generated by SCRNA-Seq and performs dimensionality reduction using Multiple Corespondance Analysis. This enables a representation of both genes and cell in the same euclidean space with genes close to a particular cell or group of cells being specificly expressed in those. This property allows to get a ranking of specificity of genes for each cells giving the opportunity to extract meaningful geneset or perform geneset enrichment analysis.

Author(s)

Maintainer: Akira Cortal [e-mail]

Authors:

• Akira Cortal

• Antonio Rausell

References

• Rausell, A., Juan, D., Pazos, F., & Valencia, A. (2010). Protein interactions and ligand binding: from protein subfamilies to functional specificity. Proceedings of the National Academy of Sciences of the United States of America, 107(5), 1995–2000. https://doi.org/10.1073/pnas.0908044107

• Aan, Z., & Greenacre, M. (2011). Biplots of fuzzy coded data. Fuzzy Sets and Systems, 183(1), 57–71. https://doi.org/10.1016/j.fss.2011.03.007

• Alexey Sergushichev. An algorithm for fast preranked gene set enrichment analysis using cumulative statistic calculation. bioRxiv (2016), https://doi.org/10.1101/060012

• Stuart and Butler et al. Comprehensive integration of single cell data. bioRxiv (2018). https://doi.org/10.1101/460147

• Aaron Lun and Davide Risso (2019). SingleCellExperiment: S4 Classes for Single Cell Data. R package version 1.4.1.

See Also

• McCarthy, D. J., Campbell, K. R., Lun, A. T. L., & Wills, Q. F. (2017). Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R. Bioinformatics, 33(8), btw777. https://doi.org/10.1093/bioinformatics/btw777

• Amezquita, R. A., Carey, V. J., Carpp, L. N., Geistlinger, L., Lun, A. T. L., Marini, F., … Hicks, S. C. (2019). Orchestrating Single-Cell Analysis with Bioconductor. BioRxiv, 590562. https://doi.org/10.1101/590562

cbl-imagine/CellID documentation built on July 22, 2020, 7:18 p.m.