cyclicNullR {DiNAMIC.Duo}

Create a cyclic shift-based null distribution for one or two copy number matrices

Description

Create a cyclic shift-based null distribution for one or two copy number matrices

Usage

cyclicNullR(X, Y = NULL, numPerms = 100, randomSeed = NULL)

Arguments

X

a matrix or a data frame of copy number data. The rows and columns of X correspond to genes and subjects, respectively.

Y

a matrix or a data frame of copy number data. The rows and columns of X correspond to genes and subjects, respectively. It is assumed that the rows of X and Y are indexed by the same set of genes that appear in genomic order.

numPerms

the number of cyclic shifts used to create the null distribution. Default = 1e2.

randomSeed

a random seed. Default = NULL.

Details

This function iteratively calls cyclicShiftColR to create an empirical permutation-based null distribution to assess the statistical significance of either (i) the maximum and minimum difference row means of X - row means of Y, or (ii) the maximum and minimum row means of X, depending on whether two or one copy number matrices are being analyzed. The application of cyclic shift permutations to DNA copy number matrices was originally described by Walter et al. (Bioinformatics, 2011;27(5):678–685).

Value

a matrix with two columns. The first column, maxNull, is an empirical permutation-based null distribution for the maximum difference of row means of X - row means of Y based on cyclic shift permutations of the columns of each matrix; the second column, minNull, is an empirical distribution of the minimum difference of the row means of X - the row means of Y based on the same permutations. If Y = NULL, the null distributions apply to the maximum and minimum row means of X.

Examples

data(DiNAMIC.Duo)
output = cyclicNullR(X = pD[["X"]], Y = pD[["Y"]], numPerms = 25, randomSeed = NULL)