# calculateCorForTwoMatrices: Identify the significant correlations between two matrices. In wangj26/multiOmicsViz: Plot the effect of one omics data on other omics data along the chromosome

## Description

The calculateCorForTwoMatrices function uses the spearman correlation to identify the significant correlations between two matrices.

## Usage

 `1` ```calculateCorForTwoMatrices(matrix1,matrix2,fdr) ```

## Arguments

 `matrix1` A R matrix, data.frame or SummarizedExperiment object containing the numeric values. `matrix2` A R matrix, data.frame or SummarizedExperiment object containing the numeric values. `matrix2` should have at least 6 overlapping samples with `matrix1`. `fdr` The FDR threshold for identifying significant correlations.

## Value

This function will return a R matrix object containing significant correlations. "1" represents the significant positive correlation, "-1" represents the significant negative correlation and "0" represents no significant correlation.

Jing Wang

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10``` ``` matrix1 <- system.file("extdata","sourceOmics.txt",package="multiOmicsViz") matrix1 <- read.table(matrix1,header=TRUE,sep="\t",stringsAsFactors=FALSE, check.names=FALSE) matrix2 <- system.file("extdata","targetOmics.txt",package="multiOmicsViz") matrix2 <- read.table(matrix2,header=TRUE,sep="\t",stringsAsFactors=FALSE, check.names=FALSE) sig <- calculateCorForTwoMatrices(matrix1=matrix1, matrix2=matrix2,fdr=0.01) ```

wangj26/multiOmicsViz documentation built on May 4, 2019, 12:58 a.m.