getIc: Get Index for Critical transition (Ic score)
In BioTIP: BioTIP: An R package for characterization of Biological Tipping-Point

Description Usage Arguments Value Author(s) References Examples

View source: R/BioTIP_update_4_09282020_v3.R

Retrieve Ic scores (Pearson correlation of genes / Pearson correlation of samples) for the identified critical transition state

getIc(
  counts,
  sampleL,
  genes,
  output = c("Ic", "PCCg", "PCCs"),
  fun = c("cor", "BioTIP"),
  shrink = TRUE,
  use = c("everything", "all.obs", "complete.obs", "na.or.complete",
    "pairwise.complete.obs")
)

`counts`	A numeric matrix or data frame. The rows and columns represent unique transcript IDs (geneID) and sample names, respectively.
`sampleL`	A list of vectors, whose length is the number of states. Each vector gives the sample names in a state. Note that the vector s (sample names) has to be among the column names of the R object 'df'.
`genes`	A character vector consisting of unique CTS gene ids. This can be obtained from `getMaxMCImember`
`output`	A string. Please select from 'Ic', 'PCCg', or 'PCCs'. Uses 'Ic' by default. 'PCCg' is the PCC between genes (numerator) and 'PCCs' is PCC between samples (denominator)
`fun`	An optional character string indicating the R functon to calculate correlations for all possible pairs of columns of a matrix. When using "BioTIP", The method is modified to ignore missing values, analogous to how `cor(X, use = "pairwise.complete.obs")` works. Note that the "BioTIP" option only function together with shrink = TRUE.
`shrink`	A flag specifying whether to shrink the correlation or not. 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) Comparing to fun='cor', the 'BioTIP' method without shinkage is modified to ignore missing values, analogous to how `cor(X, use = "pairwise.complete.obs")` works.
`use`	An optional character string, when fun=="cor", it gives a method for computing covariances in the presence of missing values. This must be (an abbreviation of) one of the strings "everything", "all.obs", "complete.obs", "na.or.complete", or "pairwise.complete.obs".

A list of numeric values, whose length and names are inherited from sampleL

Zhezhen Wang zhezhen@uchicago.edu; Xinan H Yang xyang2@uchicago.edu

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

M. Mojtahedi et al., Cell Fate Decision as High-Dimensional Critical State Transition. PLoS Biol 14, e2000640 (2016).

counts = matrix(sample(1:100, 27),  3,  9)
colnames(counts) = 1:9
row.names(counts) = c('loci1', 'loci2', 'loci3')
cli = cbind(1:9, rep(c('state1', 'state2', 'state3'), each = 3))
colnames(cli) = c('samples', 'group')
samplesL <- split(cli[, 1],  f = cli[, 'group'])
CTS = c('loci1', 'loci2')

## Comparing the results with an estiamted correlation matrix with that without estimation.
Ic = getIc(counts,  samplesL,  CTS, fun='cor')
Ic.2 = getIc(counts,  samplesL,  CTS, fun='BioTIP', shrink=FALSE)
BioTIP = getIc(counts,  samplesL,  CTS, fun='BioTIP')