This is Rcpp extension that has written by Musa-Sina-Ertugrul for BMG Lab. To install extension, install .cpp file from this github page. Then choose a location for file such as:
path_to_file/bmg_lab.cpp
After these steps open R then instal and import Rcpp (If you already installed Rcpp, you can pass installation step):
install.packages("Rcpp")Then import it:
library(Rcpp)Finally you can import bmg_lab.cpp:
Rcpp::sourceCpp("path_to_file/bmg_lab.cpp")This extension has only one main function named as run() other functions are test functions, to run run():
# B_data is dataframe that gene names are rownames
# list = B_Data_list is list format of B_Data
# row_names = rownames(B_Data)
# groups = groups_2 is group information as
# NumericVector such as c( 1,1,1,1,2,2,2,2) that data has 2 group
# nodes_1 = as.character(Edges[["X.node1"]]) one of columns in edges dataframe this must be
# CharecterVector Chracters must be names of genes and
# they must be same with row_names parameter genes
# nodes_2 = as.character(Edges[["node2"]]) same of edges_1
# alpha threshold of p_value
# D threshold for length between two nodes that are in network
final<-run( list = B_Data_list, groups = groups_2, row_names = rownames(B_Data),
nodes_1 = as.character(Edges[["X.node1"]]), nodes_2 = as.character(Edges[["node2"]]),
alpha = 0.05, D = 1000)
There is one more important function that named as S_i(). That function calculates S score nodes as topological:
# B_data is dataframe that gene names are rownames
# rowNames = rownames(B_Data)
# edges_1 = as.character(Edges[["X.node1"]]) one of columns in edges dataframe this must be
# CharecterVector Chracters must be names of genes and
# they must be same with row_names parameter genes
# edges_2 = as.character(Edges[["node2"]]) same of edges_1
# alpha threshold of p_value
# D threshold for length between two nodes that are in network
S_i( rowNames = as.character(rownames(B_Data)),edges_1 = as.vector(as.character(Edges[["X.node1"]])),
edges_2 = as.vector(as.character(Edges[["node2"]])), alpha = 0.05, D = 100)
This work based on NetworkHub article. Thanks for visiting.