getMaxMCImember: Identifying the 'Biomodule'

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

Description

This function reports the 'biomodule', which is the module with the maximum Module Critical Index (MCI) scores for each state. Each state can have multiple modules (groups of subnetworks derived from the function getCluster_methods). This function runs over all states.

Usage

getMaxMCImember(membersL, MCIl, minsize = 1)

Arguments

membersL	A list of integer vectors with unique ids as names. Each vector represents the cluster number assign to that unique id. The length of this list is equal to the number of states in the study. This can be the first element of the output from function getMCI or the output from getCluster_methods, see Examples for more detail.
MCIl	A list of numeric vectors with unique cluster numbers as names. Each vector represents the MCI scores of that module. This can be the second element of the output from function getMCI.
minsize	A numerical value of the minimum module size (the number of transcripts in a cluster) to output for downstream analysis.

Value

A nested list whose length is the length of the input object membersL. Each internal list contains two objects: one object is the vector of biomodule IDs across states, and the other object is a list of transcript IDs (each defines the biomodule per state) across states.

Author(s)

Zhezhen Wang [e-mail]

Examples

#1st option: get the input directly from getMCI function
test = list('state1' = matrix(sample(1:10, 6), 4, 3),  
     'state2' = matrix(sample(1:10, 6), 4, 3), 
     'state3' = matrix(sample(1:10, 6), 4, 3))

# assign colnames and rownames to the matrix
for(i in names(test)){
  colnames(test[[i]]) = 1:3
    row.names(test[[i]]) = c('g1', 'g2', 'g3', 'g4')}

cluster = list(c(1, 2, 2, 1), c(1, 2, 3, 1), c(2, 2, 1, 1))
names(cluster) = names(test)
for(i in names(cluster)){
  names(cluster[[i]]) = c('g1', 'g2', 'g3', 'g4')}

membersL_noweight <- getMCI(cluster, test)
maxMCIms <- getMaxMCImember(membersL_noweight[[1]],  membersL_noweight[[2]],  min =3)
#The same as
maxMCIms <- getMaxMCImember(cluster,  membersL_noweight[[2]],  min =2)

## case1: using 'rw' method by default
igraphL <- getNetwork(test,  fdr=1)
cl <- getCluster_methods(igraphL)
## make sure every element in list cl is a \code{communities} object
sapply(cl, class)
##       state1        state2        state3
##"communities" "communities" "communities"

## If there is(are) state(s) that is(are) empty which will not be a communities object(s),  
## please manually remove that state(s).

cl = cl[which(sapply(cl, class) == 'communities')]

## and then run
library(igraph)
cluster = lapply(cl,  membership)
maxCIms <- getMaxMCImember(cluster,  membersL_noweight[[2]],  min =2)

## or run function 'getMCI' and use the 1st option
membersL_noweight <- getMCI(cl, test)

## case2: using methods other than the default
cl <- getCluster_methods(test, method = "pam", cutoff = 2)
## check to make sure membersL_noweight[[2]] has values and run
maxCIms <- getMaxMCImember(cl,  membersL_noweight[[2]],  min =2)

BioTIP documentation built on Nov. 8, 2020, 6:27 p.m.