simulationMCI: Get MCI Scores for randomly selected genes

In xyang2uchicago/NPS: BioTIP: An R package for characterization of Biological Tipping-Point

Get MCI Scores for randomly selected genes

Description

This function gets the MCI scores for randomly selected features (e.g. transcript ids),

Usage

simulationMCI(
  len,
  samplesL,
  df,
  adjust.size = FALSE,
  B = 1000,
  fun = c("cor", "BioTIP")
)

Arguments

len	An integer that is the length of genes in the CTS (critical transition signal).
samplesL	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'.
df	A numeric matrix or dataframe of numerics, factor or character. The rows and columns represent unique transcript IDs (geneID) and sample names, respectively
adjust.size	A boolean value indicating if MCI score should be adjust by module size (the number of transcripts in the module) or not. Default FALSE.
B	An integer, setting the permutation with B runs. Default is 1000.
fun	A character chosen between ("cor", "BioTIP"), indicating where an adjusted correlation matrix will be used to calculated the MCI score.

Value

A numeric matrix indicating the MCI scores of permutation. The dimension (row X column) of this matrix is the length of samplesL * B.

Author(s)

Zhezhen Wang [e-mail]; Xinan H Yang [e-mail]

Examples

counts = matrix(sample(1:100, 18), 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'])
simMCI = simulationMCI(2, samplesL, counts, B=2)
simMCI
#            [,1]      [,2]
#state1  2.924194  2.924194
#state2 20.877138 20.877138
#state3  2.924194  2.924194

xyang2uchicago/NPS documentation built on Nov. 7, 2023, 1 a.m.