Multiresolution Correlation Analysis

(MCA)

Subpopulation Identification in Single Cell Data for R

An R package for the MCA visualization method from Feigelman et al. (2014).

Heterogeneities in cell populations can be analyzed by identifying behaviorally different subpopulations. A common method of doing so is by gating cell populations on a transcription factor or other gene expression value.

MCA is a tool for the discovery of subpopulations by displaying the correlation of two genes for all possible subpopulations at once.

The tool has been applied to single cell data of mouse embryonic stem cells in Filipczyk et al. (2015).

How to use the MCA R package

Install the package via

R CMD INSTALL mca_3.1.tar.gz

Afterwards you can load and use the package like this:

library(mca)
mca <- MCA(dataset, 'sorting.variable')
plot(mca, 'correlated.variable.1', 'correlated.variable.2')

you can download mca_3.1.tar.gz below:

Availability

The MCA R package is available for download here:

The web interface will be available soon.

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