getIc.new {BioTIP} | R Documentation

## Index of criticality Scoring System with estimated correlation, an updated Ic-score

### Description

This function calculates the BioTIP score on a given data matrix X (or two matrixes X and Y). It can also calculate the I_c score, if desired.

This appraoch uses the method outlined by Schafer and Strimmer in "A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics" (2005)

This approach is modified to ignore missing values, analogous to how `cor(X, use = "pairwise.complete.obs")` works.

The gene-gene correlations are shrunk towards 0, whereas the sample-sample correlations are shrunk towards their empirical average.

### Usage

    getIc.new(
      X,
      method = c("BioTIP", "Ic"),
      PCC_sample.target = c("average", "zero", "half"),
      output = c("IndexScore", "PCCg", "PCCs")
    )

### Arguments

`X` | A G x S matrix of counts. Rows correspond to genes, columns correspond to samples.
`method` | A flag specifying whether to calculate the BioTIP score or the I_c score
`PCC_sample.target` | A character choose among ('average', 'zero', 'half'), indicating whether to shrink PCC towards towards their empirical common average, zero or 0.5 (for sample-sample correlations).
`output` | A string. Please select from 'IndexScore', 'PCCg', or 'PCCs'. Uses 'IndexScore' by default. 'PCCg' is the PCC between genes (numerator) and 'PCCs' is PCC between samples (denominator).

### Value

A value containing the shrunk BioTIP or non-shrunk I_c score

### Author(s)

Andrew Goldstein andrewgoldstein@uchicago.edu

### Examples

    ## 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(1000, mu, M)
    dim(X)  #1000 10

    ## Calculating pairwise correlation between 1000 genes; then the mean value
    ## in two ways, respectively
    cor_tX = cor(t(X))
    mean(abs(cor_tX[upper.tri(cor_tX, diag = FALSE)])) # 0.9150228

    getIc.new(X, method = "Ic", output ='PCCg') # 0.9150228
    getIc.new(X, method = "BioTIP", output ='PCCg') # 0.8287838

    ## Uisng the Index of critical scoreing system, in two ways, respectively
    (newscore = getIc.new(X, method = "BioTIP"))
    (oldscore = getIc.new(X, method = "Ic"))

[Package BioTIP version 1.8.0 Index]