MRHCA is a non-parametric co-expression analysis method for large association network:
Our analysis has demonstrated MRHCA can 1. deal with large association networks, 2. rigorously assess statistical significance for hubs and module sizes, 3. identify co-expression modules with low associations, 4. detect small and significant modules, and 5. allow genes to be present in more than one modules, compared with existing methods.
The link to R and C++ codes which could reproduce the result of "MRHCA: A nonparametric statistics based method for hub and co-expression module identification in large gene co-expression network".
To install the development version of MRHCA, you will need to install at least the following packages from CRAN
install.packages("Rcpp")
For Windows users, Rtools (https://cran.r-project.org/bin/windows/Rtools/) should also be installed.
Then,
install.packages("devtools")
devtools::install_github("zy26/MRHCA")
file <- "https://github.com/zy26/mrct/raw/master/testdata/E_coli_anaerobic.txt"
x <- as.matrix(read.table(file, sep = ' ', header = TRUE, row.names = 1))
mr <- MRHCA::GetHubs(x)
If you have already obtained the results from the C++ codes, you can further use the FixHubs
function to filter and optimize the results.
datafile <- "TCGA-COAD.htseq_fpkm.tsv"
file <- "TCGA-COAD.htseq_fpkm.tsv.id.txt"
emfile <- "TCGA-COAD.htseq_fpkm.tsv.txt"
mr <- MRHCA::FixHubs(datafile, file, emfile)
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