# RMDtest: Multivariate Radial Mean Deviation Test of Variance In GSAR: Gene Set Analysis in R

## Description

Performs two-sample nonparametric multivariate test of variance based on the minimum spanning tree (MST). It calculates the mean deviation between the cumulative distribution functions (CDFs) of sample ranks in two conditions. It tests the null hypothesis that a set of features has the same variance (scale) in two conditions versus different variances.

## Usage

 1 RMDtest(object, group, mst.order=1, nperm=1000, pvalue.only=TRUE) 

## Arguments

 object a numeric matrix with columns and rows respectively corresponding to samples and features. group a numeric vector indicating group associations for samples. Possible values are 1 and 2. mst.order numeric value to indicate the consideration of the union of the first mst.order MSTs. Default value is 1. Maximum allowed value is 5. nperm number of permutations used to estimate the null distribution of the test statistic. If not given, a default value 1000 is used. pvalue.only logical. If TRUE (default), the p-value is returned. If FALSE a list of length three containing the observed statistic, the vector of permuted statistics, and the p-value is returned.

## Details

This function tests the null hypothesis that a set of features has the same scale in two conditions. It performs a two-sample nonparametric multivariate test based on the minimum spanning tree (MST) as proposed by Friedman and Rafsky (1979). The MST of the weighted undirectional graph created from the samples is found. The nodes of the MST are ranked based on their position in the MST. The MST is rooted at the node with smallest geodisic distance (rank 1) and nodes with higher depths from the root are assigned higher ranks. The mean deviation between the cumulative distribution functions (CDFs) of sample ranks in two conditions is calculated. The null distribution of the test statistic is estimated by permuting sample labels nperm times and calculating the test statistic for each. P-value is calculated as

p.value = \frac{∑_{k=1}^{nperm} I ≤ft[ |D_{k}| ≥q |D_{obs}| \right] + 1}{nperm + 1}

where D_{k} is the test statistic for permutation k, D_{obs} is the observed test statistic, and I is the indicator function.

## Value

When pvalue.only=TRUE (default), function RMDtest returns the p-value indicating the attained significance level. When pvalue.only=FALSE, function RMDtest produces a list of length 3 with the following components:

 statistic the value of the observed test statistic. perm.stat numeric vector of the resulting test statistic for nperm random permutations of sample labels. p.value p-value indicating the attained significance level.

## Note

The variance of both the Poisson and negative Bionomial distributions, used to model count data, is a function of their mean. Therefore, using the radial mean deviation test (RMDtest) to detect pathways with differential variance for RNA-Seq counts is not recommended without proper data normalization.

## Author(s)

Yasir Rahmatallah and Galina Glazko

## References

Rahmatallah Y., Emmert-Streib F. and Glazko G. (2012) Gene set analysis for self-contained tests: complex null and specific alternative hypotheses. Bioinformatics 28, 3073–3080.

Friedman J. and Rafsky L. (1979) Multivariate generalization of the Wald-Wolfowitz and Smirnov two-sample tests. Ann. Stat. 7, 697–717.

RKStest, WWtest, MDtest, KStest.
  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ## generate a feature set of length 20 in two conditions ## each condition has 20 samples ## use multivariate normal distribution library(MASS) ngenes <- 20 nsamples <- 40 ## let the mean vector have zeros of length 20 for both conditions zero_vector <- array(0,c(1,ngenes)) ## set the covariance matrix to be an identity matrix for condition 1 cov_mtrx <- diag(ngenes) gp1 <- mvrnorm((nsamples/2), zero_vector, cov_mtrx) ## set some scale difference in the covariance matrix for condition 2 cov_mtrx <- cov_mtrx*3 gp2 <- mvrnorm((nsamples/2), zero_vector, cov_mtrx) ## combine the data of two conditions into one dataset gp <- rbind(gp1,gp2) dataset <- aperm(gp, c(2,1)) ## first 20 samples belong to group 1 ## second 20 samples belong to group 2 pvalue <- RMDtest(object=dataset, group=c(rep(1,20),rep(2,20)))