# correlationTest: Correlation of each row of a matrix or MRexperiment object In metagenomeSeq: Statistical analysis for sparse high-throughput sequencing

## Description

Calculates the (pairwise) correlation statistics and associated p-values of a matrix or the correlation of each row with a vector.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```correlationTest( obj, y = NULL, method = "pearson", alternative = "two.sided", norm = TRUE, log = TRUE, cores = 1, override = FALSE, ... ) ```

## Arguments

 `obj` A MRexperiment object or count matrix. `y` Vector of length ncol(obj) to compare to. `method` One of 'pearson','spearman', or 'kendall'. `alternative` Indicates the alternative hypothesis and must be one of 'two.sided', 'greater' (positive) or 'less'(negative). You can specify just the initial letter. `norm` Whether to aggregate normalized counts or not - if MRexperiment object. `log` Whether or not to log2 transform the counts - if MRexperiment object. `cores` Number of cores to use. `override` If the number of rows to test is over a thousand the test will not commence (unless override==TRUE). `...` Extra parameters for mclapply.

## Value

A matrix of size choose(number of rows, 2) by 2. The first column corresponds to the correlation value. The second column the p-value.

`correctIndices`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16``` ```# Pairwise correlation of raw counts data(mouseData) cors = correlationTest(mouseData[1:10,],norm=FALSE,log=FALSE) head(cors) mat = MRcounts(mouseData)[1:10,] cormat = as.matrix(dist(mat)) # Creating a matrix cormat[cormat>0] = 0 # Creating an empty matrix ind = correctIndices(nrow(mat)) cormat[upper.tri(cormat)][ind] = cors[,1] table(cormat[1,-1] - cors[1:9,1]) # Correlation of raw counts with a vector (library size in this case) data(mouseData) cors = correlationTest(mouseData[1:10,],libSize(mouseData),norm=FALSE,log=FALSE) head(cors) ```