# qpPCC: Estimation of Pearson correlation coefficients In qpgraph: Estimation of genetic and molecular regulatory networks from high-throughput genomics data

## Description

Estimates Pearson correlation coefficients (PCCs) and their corresponding P-values between all pairs of variables from an input data set.

## Usage

 ```1 2 3 4 5 6``` ```## S4 method for signature 'ExpressionSet' qpPCC(X) ## S4 method for signature 'data.frame' qpPCC(X, long.dim.are.variables=TRUE) ## S4 method for signature 'matrix' qpPCC(X, long.dim.are.variables=TRUE) ```

## Arguments

 `X` data set from where to estimate the Pearson correlation coefficients. It can be an ExpressionSet object, a data frame or a matrix. `long.dim.are.variables` logical; if TRUE it is assumed that when `X` is a data frame or a matrix, the longer dimension is the one defining the random variables (default); if FALSE, then random variables are assumed to be at the columns of the data frame or matrix.

## Details

The calculations made by this function are the same as the ones made for a single pair of variables by the function `cor.test` but for all the pairs of variables in the data set, with the exception of the treatment of missing values, since only complete observations across all variables in `X` are used.

## Value

A list with two matrices, one with the estimates of the PCCs and the other with their P-values.

## Author(s)

R. Castelo and A. Roverato

`qpPAC`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23``` ```require(graph) require(mvtnorm) nVar <- 50 ## number of variables nObs <- 10 ## number of observations to simulate set.seed(123) g <- randomEGraph(as.character(1:nVar), p=0.15) Sigma <- qpG2Sigma(g, rho=0.5) X <- rmvnorm(nObs, sigma=as.matrix(Sigma)) pcc.estimates <- qpPCC(X) ## get the corresponding boolean adjacency matrix A <- as(g, "matrix") == 1 ## Pearson correlation coefficients of the present edges summary(abs(pcc.estimates\$R[upper.tri(pcc.estimates\$R) & A])) ## Pearson correlation coefficients of the missing edges summary(abs(pcc.estimates\$R[upper.tri(pcc.estimates\$R) & !A])) ```