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tooManyCellsR

Website

This package is an R wrapper around too-many-cells. See https://github.com/GregorySchwartz/too-many-cells for latest version of the program this wrapper targets.

See the publication (and please cite!) for more information about the algorithm.

img/pruned_tree.png

Description

too-many-cells is a suite of tools, algorithms, and visualizations focusing on the relationships between cell clades. This includes new ways of clustering, plotting, choosing differential expression comparisons, and more! TooManyCellsR is an R wrapper around too-many-cells to facilitate ease-of-use for R users. This package requires =too-many-cells= to be installed and in your path! See https://github.com/GregorySchwartz/too-many-cells for detailed installation instructions.

Installation

First install too-many-cells using the instructions in the documentation. Next, install this packages from github:

install.packages("devtools")
library(devtools)
install_github("GregorySchwartz/tooManyCellsR")

Usage

This package allows for all features from too-many-cells to be used in R, with a focus on the make-tree entry point. For more information about the different options, see the documentation for make-tree with included examples at https://github.com/GregorySchwartz/too-many-cells.

The input matrix should be of Matrix format from the Matrix library, with cell barcode column names and features (genes) as row names. The labels argument takes a data frame with item (cell barcodes) and label (whatever labels you want to give them, such as tissue of origin, celltype, etc.) columns.

The args argument contains a list of command line arguments fed to too-many-cells. The default is to just have make-tree as an argument. For a detailed list, check too-many-cells make-tree -h and check out the documentation at https://github.com/GregorySchwartz/too-many-cells.

The main function to use is tooManyCells. For example:

tooManyCells(mat, args = c("make-tree", "--smart-cutoff", "4", "--min-size", "1"))
plot(res$treePlot, axes = FALSE)
res$stdout
res$nodeInfo
plot(res$clumpinessPlot, axes = FALSE)