corr.list.compute: A Function for Creating a List of Pearson Correlation...
In MVisAGe: Compute and Visualize Bivariate Associations

Description Usage Arguments Value Examples

This function uses the corr.compute() function to compute gene-specific Pearson correlation coefficients in each group of samples defined in a sample annotation matrix.

1 2	corr.list.compute(exp.mat, cn.mat, gene.annot, sample.annot = NULL, method = "pearson", digits = 5, 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.
`sample.annot`	An optional two-column matrix of sample annotation data. Column 1 = sample IDs, Column 2 = sample annotation (e.g. tumor vs. normal). If NULL, sample annot will be created using the common sample IDs and a single group ('1'). Default = NULL.
`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).
`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 list whose length is the number of unique groups defined by sample.annot. Each entry in the list is the output of corr.compute.

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)

 pd.exp = prepped.data[["exp"]]

 pd.cn = prepped.data[["cn"]]

 pd.ga = prepped.data[["gene.annot"]]

 pd.sa = prepped.data[["sample.annot"]]

 corr.list.compute(pd.exp, pd.cn, pd.ga, pd.sa)