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

In Cortalak/cellID: Unbiased Extraction of Single Cell Identity using Multiple Correspondence Analysis

Description

The exhaustive exploration of human cell heterogeneity requires the unbiased identification of molecular signatures that can serve as unique cell identity cards for every cell in the body. However, the stochasticity associated with high-throughput single-cell RNA sequencing has made it necessary to use clustering-based computational approaches in which the transcriptional characterization of cell-type heterogeneity is performed at cell-subpopulation level rather than at full single-cell resolution. We present here Cell-ID, a clustering-free multivariate statistical method for the robust extraction of per-cell gene signatures from single-cell RNA-seq. Cell-ID allows unbiased cell identity recognition across different donors, tissues-of-origin, model organisms and single-cell omics protocols. Cell-ID is distributed as an open-source R software package: https://github.com/RausellLab/CelliD.

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

Cortalak/cellID documentation built on Aug. 3, 2020, 9:01 p.m.