getMCI {BioTIP} | R Documentation |
This function calculates a module critical index (MCI) score for
each module per state within a dataset. Each module is a cluster of
transcripts generated from the function getCluster_methods
.
Note that a dataset should contains three or more states (samples in
groups).
getMCI(groups, countsL, adjust.size = FALSE)
groups |
A list of elements whose length is the member of states. The
elements could be either be vectors or |
countsL |
A list of x numeric count matrices or x data frame, where x is the number of states. |
adjust.size |
A boolean value indicating if MCI score should be adjusted by module size (the number of transcripts in the module) or not. Default FALSE. |
A list of five elements (members, MCI, Sd, PCC, and PCCo). Each of
element is a two-layer nested list whose length is the length of the input
object groups
. Each internal nested list is structured according to
the number of modules identified in that state.
members: vectors of unique ids
MCI: the MCI score
sd: standard deviation
PCC: Mean of pairwised Pearson Correlation Coefficient calculated among the loci in a module.
PCCo: Mean of pairwised Pearson Correlation Coefficient calculated between the loci in a module and the loci outside that module but inside the same state.
Zhezhen Wang zhezhen@uchicago.edu
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)