esti_par: Estimate parameters of BNs in control and disease states

In BNrich: Pathway Enrichment Analysis Based on Bayesian Network

Description

Estimate parameters of BNs in control and disease states

Usage

esti_par(BN_H, BN_D, data_h, data_d)

Arguments

BN_H	A list contains simplified BNs structures for control objects
BN_D	A list contains simplified BNs structures for disease objects
data_h	A list contains data frames related to control objects for any BN
data_d	A list contains data frames related to disease objects for any BN

Value

A listcontains four lists BNs_h, BNs_d, coef_h and coef_d

Examples

#All the 187 preprocessed signaling pathways can be entered in analysis by fetch_data_file().
#But here you enter a subset of those pathways to see how this package works.
files <- system.file("extdata", "test_files_to_start.RData", package = "BNrich", mustWork = TRUE)
load(files)
Data <- system.file("extdata", "Test_DATA.RData", package = "BNrich", mustWork = TRUE)
load(Data)
uni_Result <- unify_path(dataH, dataD, MapkG = sub_mapkG, Pathway.id = path.id)
M1 <- uni_Result$mapkG1
BN <- BN_struct(M1)
data_h1 <- uni_Result$data_h
data_d1 <- uni_Result$data_d
LASSO_Result <- LASSO_BN(BN = BN , data_h = data_h1 , data_d = data_d1)
BN_h1 <- LASSO_Result$BN_h
BN_d1 <- LASSO_Result$BN_d
esti_result <- esti_par(BN_H = BN_h1, BN_D = BN_d1, data_h = data_h1, data_d = data_d1)

BNrich documentation built on April 14, 2020, 7:08 p.m.