NOGEA: Network-Oriented Gene Entropy Approach for Dissecting Disease Comorbidity and Drug Repositioning

Rapid development of high-throughput technologies has permitted the identification of an increasing number of disease-associated genes (DAGs), which are important for understanding disease initiation and developing precision therapeutics. However, DAGs often contain large amounts of redundant or false positive information, leading to difficulties in quantifying and prioritizing potential relationships between these DAGs and human diseases. Here, a network-oriented gene entropy approach (NOGEA) is proposed for accurately inferring master genes that contribute to specific diseases by quantitatively calculating their perturbation abilities on directed disease-specific gene networks.

Schema

Version

1.0.1

Date

2020.04.03

Title

NOGEA: Network-Oriented Gene Entropy Approach for Dissecting Disease Comorbidity and Drug Repositioning

Author

Zihu Guo
Address: Center of Bioinformatics, Northwest A & F University

Maintainer

Zihu Guo
Email: guozihu2010@yahoo.com

Description

Network-Oriented Gene Entropy Approach for Dissecting Disease Comorbidity and Drug Repositioning

Required packages

• R (>3.6.0)
• igraph (>1.2.4)
• ROCR
• ggplot2
• beanplot
• dplyr (1.4.2)

Reference manual

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• mutildisease_entropy calcualte gene entropy for each DAG in each disease.

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Description

Calcualte gene entropy for each DAG in each disease

Usage

mutildisease_entropy(dagassociation = disease_gene,
                                     diseaseids = names(disease_gene),
                                     outdegree = myoutdegree,
                                     dagnetwork.distance = dagnetwork.distance,
                                     dagbackground = all_genes,
                                     alpha = 1.1,
                                     scales = T)

Arguments

• dagassociation a list of DAG vectors. Each DAG vector contain the gene names of the DAGs for that disease.
• diseaseids the diseases selected from dagassociation, which you want to obtain the gene entropy values
• outdegree out degree of each DAG in the directed PPI network
• dagbackground the gene names of proteins that are involved in the directed PPI network
• alpha the scale paratemter to convert distance to probability
• scales whether scale the entropy value for each disease or not

Details

Value

An object of the class list is a list containing the entropy values of DAGs in each disease

Examples

entropy_list <- mutildisease_entropy(dagassociation = disease_gene,
                                     diseaseids = names(disease_gene),
                                     outdegree = myoutdegree,
                                     dagnetwork.distance = dagnetwork.distance,
                                     dagbackground = all_genes,
                                     alpha = 1.1,
                                     scales = T)

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• multi_DAG_class *Disease-gene classification based on the gene entropy value.

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Description

Disease-gene classification based on the gene entropy value

Usage

multi_DAG_class(entropy_list = entropy_list)

Arguments

• entropy_list the DAG entropy values for multiple diseases obtained with the function mutildisease_entropy

Details

Value

An object of the class data.frame is a dataframe containing the entropy values and classfication of DAGs in each disease

Examples

dag_class_result <- multi_DAG_class(entropy_list = entropy_list)

Example to DAG entropy calculation and classification

• Open the file NOGEA.Rproj with Rstudio, you can find an complete example for DAG entropy calculation and gene classification in 000-main.R

Import the data, libraries and functions used in this research

source("010-usefullibrary.R")
source("020-usefulfunction.R")
source("101-importdata.R")

Calcualte gene entropy for each DAG in each disease

entropy_list <- mutildisease_entropy(dagassociation = disease_gene,
                                     diseaseids = names(disease_gene),
                                     outdegree = myoutdegree,
                                     dagnetwork.distance = dagnetwork.distance,
                                     dagbackground = all_genes,
                                     alpha = 1.1,
                                     scales = T)

Disease-gene classification based on the gene entropy value

dag_class_result <- multi_DAG_class(entropy_list = entropy_list)

Citation

if you use NOGEA or NOGEA related methods please cite:
Guo Z, Fu Y, Huang C, Zheng C, Wu Z, Chen X, et al. NOGEA: Network-Oriented Gene Entropy Approach for Dissecting Disease Comorbidity and Drug Repositioning. bioRxiv. 2020:2020.04.01.019901. doi: 10.1101/2020.04.01.019901.