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multicross (version 2.0.0)

A Graph-Based Test for Comparing Multivariate Distributions in the Multi Sample Framework

Description

We introduce a nonparametric, graphical test based on optimal matching for assessing whether multiple unknown multivariate probability distributions are equal. This method is consistent, and does not make any distributional assumptions on the data. Our procedure combines data that belong to different classes or groups to create a graph on the pooled data, and then utilizes the number of edges connecting data points from different classes to examine equality of distributions among the classes. The functions available through this package implement the work described here: .

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Install

install.packages('multicross')

Monthly Downloads

34

Version

2.0.0

License

GPL (>= 2)

Maintainer

Divyansh Agarwal

Last Published

September 23rd, 2019

Functions in multicross (2.0.0)

Split a data frame or matrix into subsets based on a particular categorical variable

mhccexecutelong

Calculates the pairwise crosscounts for the K classes being examined

Creates the null covariance matrix for mmcm, corresponding to the scenario when all K distributions are the same

Use the Mahalnobis-type multisample test based on optimal matching to compare K different multivariate distributions

Given two input matrices with the same number of observations but differrent number of variables, this function returns the largest canonical correlation between variables of matrix 1 (X) and those of matrix 2 (Y).

This function takes an scRNAseq counts matrix as an input (cells in rows x genes in cols) and outputs a matrix of cells x 3 covariates (number of genes detected, sequencing depth and mitochondrial gene expression). This covariate matrix can then be used to match cells and perform stratified permutation.

Multisample generalization of Rosenbaum's crossmatch test

When the MCM/MMCM tests reject the null, class selection can help determine which of the K classes are the likely contributors for rejection

This function takes an scRNAseq counts matrix as an input (cells in rows x genes in cols) and outputs a list of cells x pathway matrices