README.md

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

Cell-ID v1.00

R package for gene signature extraction and cell-identity recognition at individual cell level from single-cell RNA-seq.

Welcome to the official Github repository of the Cell-ID software presented at the BioRxiv preprint "Cell-ID: gene signature extraction and cell identity recognition at individual cell level". Cortal A, Martignetti L, Six E, Rausell A. BioRxiv 2020.

Overview

Cell-ID is a robust statistical method that performs gene signature extraction and functional annotation for each individual cell in a single-cell RNA-seq dataset. Cell-ID is based on Multiple Correspondence Analysis (MCA) and produces a simultaneous representation of cells and genes in a low dimension space. Genes are then ranked by their distance to each individual cell, providing unbiased per-cell gene signatures. Such signatures proved valuable to (i) correctly predict cell type labels at individual cell resolution, (ii) correctly match cells from the same cell type across independent datasets, overcoming batch effects arising from different technologies, tissues-of-origin and donors, and (iii) uncover functionally relevant cell heterogeneity that would have been missed by clustering-based approaches. Cell-ID enables the robust identification of rare or even unique cells whose gene signatures are reproducible across diverse single-cell omics datasets.

Installation

Cell-ID is provided as an R package (R version >= 3.6). It contains dependencies with several Biocondutor packages as described in the Description file

Within R, set first:

install.packages("devtools")
setRepositories(ind = c(1,2,3))

To install Cell-ID then just type:

devtools::install_github("cbl-imagine/CellID")

Known installation issues

MAC OS users might experience installation issues related to Gfortran library. To solve such issue download and install the appropriate gfortran dmg file from https://github.com/fxcoudert/gfortran-for-macOS

Data input formats

Cell-ID use as input single cell data in the form of specific S4objects. Curreltly supported files are SingleCellExperiment from Bioconductor and Seurat Version 3 from CRAN.

Vignettes

A vignette illustrating Cell-ID step-by-step procedures is provided here. Applications include MCA dimensionality reduction, per-cell gene signatures extraction, automatic cell type prediction using marker gene lists, label-transferring across datasets and functional enrichment analysis.

Authors

• Akira Cortal - [e-mail]
• Antonio Rausell - [e-mail]

License

This project is licensed under the GNU General Public License 3 - see the LICENSE file for details

References

Cell-ID: gene signature extraction and cell identity recognition at individual cell level. Cortal A, Martignetti L, Six E, Rausell A. BioRxiv 2020

Companion Github repository CellIDPaperScript

Upon publication of the Cell-ID paper, a companion Github repository will be released containing all R scripts and intermediate data representations required to reproduce all figures in the manuscript

Updates

You may follow us in Twitter for regular updates: https://twitter.com/AntonioRausell

cbl-imagine/cellID documentation built on July 24, 2020, 9:35 p.m.