calculateCorr {NoRCE} | R Documentation

## Calculates the correlation coefficient values between two custom expression data.

### Description

Calculates the correlation coefficient values between two custom expression data.

### Usage

    calculateCorr(
      exp1,
      exp2,
      label1 = "",
      label2 = "",
      corrMethod = "pearson",
      varCutoff = 0.0025,
      corCutoff = 0.3,
      pcut = 0.05,
      alternate = "greater",
      conf = 0.95
    )

### Arguments

`exp1` | Custom expression data matrix or SummarizedExperiment data. Columns must be genes and rows must be patients.
`exp2` | Custom expression data matrix or SummarizedExperiment data. Columns must be genes and rows must be patients.
`label1` | Gene names of the custom exp1 expression data. If it is not provided, column name of the exp1 data will be taken.
`label2` | Gene names of the custom exp2 expression data. If it is not provided, column name of the exp2 data will be taken.
`corrMethod` | Correlation coeffient method that will be used for evaluation. Possible values are "pearson", "kendall", "spearman"
`varCutoff` | Variance cut off that genes have less variance than this value will be trimmed
`corCutoff` | Correlation cut off values for the given correlation method
`pcut` | P-value cut off for the correlation values
`alternate` | Holds the alternative hypothesis and "two.sided", "greater" or "less" are the possible values.
`conf` | Confidence level for the returned confidence interval. It is only used for the Pearson correlation coefficient if there are at least 4 complete pairs of observations.

### Value

Pairwise relations between gene-gene with corresponding correlation value and pvalue

### Examples

    #Assume that mirnanorce and mrnanorce are custom patient by gene data
    a<-calculateCorr(exp1 = mirna, exp2 = mrna )

[Package NoRCE version 1.6.0 Index]