| Type: | Package |
| Title: | Pathway Enrichment Analysis Based on Bayesian Network |
| Version: | 0.1.1 |
| Maintainer: | Samaneh Maleknia <maleknias@gmail.com> |
| Description: | Maleknia et al. (2020) <doi:10.1101/2020.01.13.905448>. A novel pathway enrichment analysis package based on Bayesian network to investigate the topology features of the pathways. firstly, 187 kyoto encyclopedia of genes and genomes (KEGG) human non-metabolic pathways which their cycles were eliminated by biological approach, enter in analysis as Bayesian network structures. The constructed Bayesian network were optimized by the Least Absolute Shrinkage Selector Operator (lasso) and the parameters were learned based on gene expression data. Finally, the impacted pathways were enriched by Fisher’s Exact Test on significant parameters. |
| biocViews: | NetworkEnrichment, GeneExpression, Pathways, Bayesian, KEGG |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.1.0 |
| Imports: | bnlearn, corpcor, glmnet, graph, stats, utils |
| Suggests: | knitr, rmarkdown |
| VignetteBuilder: | knitr |
| URL: | https://github.com/Samaneh-Bioinformatics/BNrich |
| BugReports: | https://github.com/Samaneh-Bioinformatics/BNrich/issues |
| NeedsCompilation: | no |
| Packaged: | 2020-04-04 07:17:44 UTC; Shawshank |
| Author: | Samaneh Maleknia [aut, cre], Mohsen Namazi [aut], Kaveh Kavousi [ctb], Ali Sharifi-Zarchi [ctb], Vahid Rezaei-Tabar [ctb] |
| Depends: | R (≥ 3.5.0) |
| Repository: | CRAN |
| Date/Publication: | 2020-04-04 08:50:02 UTC |
Construct Bayesian networks structures
Description
Construct BNs structures using unified signaling pathways
Usage
BN_struct(mapkG1)
Arguments
mapkG1 |
A list contains unified signaling pathways |
Value
A list contains Bayesian networks structures
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$mapkG1
BN <- BN_struct(M1)
Analysis of significant final BNs
Description
Fisher's exact test applied to PEA on final BNs
Usage
BNrich(Ttest_Results, Pathway.id1, PathName_Final, fdr.value = 0.05)
Arguments
Ttest_Results |
A data frame contains T-test results for all parameters |
Pathway.id1 |
A vector contains modified KEEG pathway IDs |
PathName_Final |
A data frame contains is IDs and names of KEEG pathways |
fdr.value |
A numeric threshold to determine significant parameters |
Value
A data frame contains fisher test results for any final pathways
Examples
#All the 187 preprocessed signaling pathways can be entered in analysis by fetch_data_file().
#But here you enter a subset of those pathways to see how this package works.
files <- system.file("extdata", "test_files_to_start.RData", package = "BNrich", mustWork = TRUE)
load(files)
Data <- system.file("extdata", "Test_DATA.RData", package = "BNrich", mustWork = TRUE)
load(Data)
uni_Result <- unify_path(dataH, dataD, MapkG = sub_mapkG, Pathway.id = path.id)
M1 <- uni_Result$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)
BNs_H <- esti_result$BNs_h
BNs_D <- esti_result$BNs_d
coef_h <- esti_result$coef_h
coef_d <- esti_result$coef_d
var_result <- var_mat(data_h1, coef_h, BNs_H, data_d1, coef_d, BNs_D)
Var_H = var_result$var_mat_Bh
Var_D = var_result$var_mat_Bd
path.id1 <- uni_Result$pathway.id1
Ttest_result <- parm_Ttest(data_h1, coef_h, BNs_H, data_d1, coef_d, BNs_D, Var_H, Var_D, path.id1)
BNrich_result <- BNrich(Ttest_result, path.id1, Path.Name)
LASSO regression
Description
LASSO regression – second step of simplification of BNs structures
Usage
LASSO_BN(BN, data_h, data_d)
Arguments
BN |
A list of Bayesian networks achieved by BN_struct function |
data_h |
A list contains data frames related to control objects |
data_d |
A list contains data frames related to disease objects |
Value
A list contains two lists.BN_h and BN_d are simplified BNs
Examples
#All the 187 preprocessed signaling pathways can be entered in analysis by fetch_data_file().
#But here you enter a subset of those pathways to see how this package works.
files <- system.file("extdata", "test_files_to_start.RData", package = "BNrich", mustWork = TRUE)
load(files)
Data <- system.file("extdata", "Test_DATA.RData", package = "BNrich", mustWork = TRUE)
load(Data)
uni_Result <- unify_path(dataH, dataD, MapkG = sub_mapkG, Pathway.id = path.id)
M1 <- uni_Result$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)
Estimate parameters of BNs in control and disease states
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)
Download data file
Description
Download necessary data file to start BNrich
Usage
fetch_data_file(destfile, verbose = FALSE)
Arguments
destfile |
A directory in user's own computer for save preprocessed data file |
verbose |
A logical argument to show verbose results |
Value
A list contain mapkG, PathName_final and pathway.id. The mapkG is a list contains imported 187 preprocessed signaling pathways, PathName_final is a data.frame includes names and IDs of all 187 pathways and pathway.id is a character vector of pathways IDs
Examples
destfile = tempfile("files", fileext = ".rda")
files <- fetch_data_file()
load(destfile)
Testing the equality regression coefficients
Description
t-test for equality the corresponging parameters in any BN
Usage
parm_Ttest(
Data_h,
coef_H,
BNs_H,
Data_d,
coef_D,
BNs_D,
Var_mat_Bh,
Var_mat_Bd,
Pathway.id1
)
Arguments
Data_h |
A list contains data frames related to control objects for any BN |
coef_H |
A list contains parameters of BN_h |
BNs_H |
A list contains BNs learned by control objects data |
Data_d |
A list contains data frames related to disease objects for any BN |
coef_D |
A list contains parameters of BN_d |
BNs_D |
A list contains BNs learned by disease objects data |
Var_mat_Bh |
A list contains covariance matrixes for any node of BN_h |
Var_mat_Bd |
A list contains covariance matrixes for any node of BN_d |
Pathway.id1 |
A vector contains modified KEEG pathway IDs |
Value
A data frame contains T-test results for all parameters in final BNs
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)
BNs_H <- esti_result$BNs_h
BNs_D <- esti_result$BNs_d
coef_h <- esti_result$coef_h
coef_d <- esti_result$coef_d
var_result <- var_mat(data_h1, coef_h, BNs_H, data_d1, coef_d, BNs_D)
Var_H = var_result$var_mat_Bh
Var_D = var_result$var_mat_Bd
path.id1 <- uni_Result$pathway.id1
Ttest_result <- parm_Ttest(data_h1, coef_h, BNs_H, data_d1, coef_d, BNs_D, Var_H, Var_D, path.id1)
Simplification networks – applied to unifying nodes
Description
Unifying nodes based imported signaling pathways and GE data
Usage
unify_path(dataH, dataD, MapkG, Pathway.id)
Arguments
dataH |
A data frame contains (healthy) control objects data |
dataD |
A data frame contains disease objects data |
MapkG |
A list contains imported 187 signaling pathways |
Pathway.id |
A vector contains 187 KEEG pathway IDs |
Value
A list contain data_h,data_d,mapkG1 and pathway.id1
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)
Estimate variance-covariance matrixes for any parameters of BNs
Description
Estimate variance-covariance matrixes for any parameters of
Usage
var_mat(Data_h, coef_H, BNs_H, Data_d, coef_D, BNs_D)
Arguments
Data_h |
A list contains data frames related to control objects for any BN |
coef_H |
A lists of parameters of BN_h achieved |
BNs_H |
A list of BNs learned by control objects data |
Data_d |
A list contains data frames related to disease objects for any BN |
coef_D |
A lists of parameters of BN_d |
BNs_D |
A list of BNs learned by disease objects data |
Value
A listcontains two lists var_mat_Bh and var_mat_Bd
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)
BNs_H <- esti_result$BNs_h
BNs_D <- esti_result$BNs_d
coef_h <- esti_result$coef_h
coef_d <- esti_result$coef_d
var_result <- var_mat(data_h1, coef_h, BNs_H, data_d1, coef_d, BNs_D)