getIc.new: Index of criticality Scoring System with estimated...
In BioTIP: BioTIP: An R package for characterization of Biological Tipping-Point

Description Usage Arguments Value Author(s) Examples

View source: R/BioTIP_update_4_09282020_v3.R

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.

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

`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).

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

Andrew Goldstein andrewgoldstein@uchicago.edu

## 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"))