perm.significance: A Function for Computing a Vector of Pearson Correlation...
In MVisAGe: Compute and Visualize Bivariate Associations

Description Usage Arguments Value Examples

This function computes Pearson correlation coefficients on a row-by-row basis for two numerical input matrices of the same size.

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perm.significance(exp.mat, cn.mat, gene.annot, method = "pearson",
  digits = 5, num.perms = 100, random.seed = NULL,
  alternative = "greater")

`exp.mat`	A matrix of gene-level expression data (rows = genes, columns = samples). Missing values are not permitted.
`cn.mat`	A matrix of gene-level DNA copy number data (rows = genes, columns = samples). Both genes and samples should appear in the same order as exp.mat. Missing values are not permitted.
`gene.annot`	A three-column matrix containing gene position information. Column 1 = chromosome number written in the form 'chr1' (note that chrX and chrY should be written chr23 and chr24), Column 2 = position (in base pairs), Column 3 = cytoband. Genes should appear in the same order as exp.mat and cn.mat.
`method`	A character string (either "pearson" or "spearman") specifying the method used to calculate the correlation coefficient (default = "pearson").
`digits`	Used with signif() to specify the number of significant digits (default = 5).
`num.perms`	Number of permutations used to assess significance (default = 1e2).
`random.seed`	Random seed (default = NULL).
`alternative`	A character string ("greater" or "less") that specifies the direction of the alternative hypothesis, either rho > 0 or rho < 0 (default = "greater").

Returns a five-column matrix. The first three columns are the same as gene.annot. The fourth column contains gene-specific Pearson or Spearman correlation coefficients based on the entries in each row of exp.mat and cn.mat, respectively (column name = "R"). The fifth column contains squared Pearson correlation coefficients (column name = "R^2"). The sixth column contains the permutation-based right-tailed p-value of the correlation coefficient (column name = "perm_pValue"). The seventh column contains Benjamini-Hochberg q-values corresponding to the p-values. Genes with constant gene expression or DNA copy number are removed because they have zero variance.

exp.mat = tcga.exp.convert(exp.mat)

 cn.mat = tcga.cn.convert(cn.mat)

 prepped.data = data.prep(exp.mat, cn.mat, gene.annot, sample.annot, log.exp = FALSE)

 perm.significance(prepped.data[["exp"]], prepped.data[["cn"]], prepped.data[["gene.annot"]])