cor.shrink {BioTIP} | R Documentation

## Estimation of correlation matrix

### Description

This function takes in one (or two) matrix X (rows are genes, columns are samples) (or Y). It then calculates the average pairwise correlation between genes or samples. This method uses the method outlined by Schafer and Strimmer (2005).

### Usage

    cor.shrink(
      X,
      Y = NULL,
      MARGIN = c(1, 2),
      shrink = TRUE,
      target = c("zero", "average", "half")
    )

### Arguments

`X` | A G1 x S matrix of counts. Rows correspond to genes, columns correspond to samples.
`Y` | A G2 x S matrix of counts. Rows correspond to genes, columns correspond to samples. By default is NULL.
`MARGIN` | An integer indicate whether the rows (1, genes) or the columns (2, samples) to be calculated for pairwise correlation.
`shrink` | A flag specifying whether to shrink the correlation or not.
`target` | A character choose among ('zero', 'average', 'half'), indicating whether to shrink towards zero (for gene-gene correlations), shrink towards their empirical common average, one or 0.5 (for sample-sample correlations).

### Value

The pairwise correlation between genes or samples. If Y==NULL, a G1 x G1 matrix is returned; otherwise, a G1 x G2 matrix is returned.

### Author(s)

Andrew Goldstein andrewgoldstein@uchicago.edu

### References

Schafer and Strimmer (2005) "A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics"

### Examples

    require(MASS)
    ## Generating a data X as coming from a multivariate normal distribution
    ## with 10 highly correlated variables, roughly simulating correlated genes.
    M = matrix(.9, nrow = 10, ncol = 10)
    diag(M) = 1
    mu = rnorm(10)
    X = MASS::mvrnorm(500, mu, M)
    dim(X)  #500 10

    ## Calculating pairwise correlation among 500 correlated genes
    cor_tX = cor(t(X))
    mean(abs(cor_tX[upper.tri(cor_tX, diag = FALSE)])) # 0.9468098

    ## Calculating estimated pairwise correlation among 500 correlated genes
    cor.matrix <- cor.shrink(X, MARGIN=1, shrink = TRUE, targe='zero') # 0.8287838
    dim(cor.matrix)   #[1] 500 500
    mean(upper.tri(cor.matrix, diag=FALSE))  # 0.499

    ## Calculating stimated pairwise correlation among 500 correlated genes
    ## and additional 100 random genes
    Y = rbind(X, matrix(rnorm(300*10), nrow = 300))
    dim(Y)  #800 10
    cor.matrix <- cor.shrink(X, Y, MARGIN=1, shrink = TRUE, targe='zero')
    dim(cor.matrix)   #[1] 500 800
    mean(upper.tri(cor.matrix, diag=FALSE))  # 0.6868

[Package BioTIP version 1.2.0 Index]