| Title: | Candidate Gene Prioritization for Non-Communicable Diseases Based on Functional Information |
|---|---|
| Description: | In gene sequencing methods, the topological features of protein-protein interaction (PPI) networks are often used, such as ToppNet <https://toppgene.cchmc.org>. In this study, a candidate gene prioritization method was proposed for non-communicable diseases considering disease risks transferred between genes in weighted disease PPI networks with weights for nodes and edges based on functional information. |
| Authors: | Erqiang Hu [aut, cre] |
| Maintainer: | Erqiang Hu <[email protected]> |
| License: | Artistic-2.0 |
| Version: | 1.0.1 |
| Built: | 2024-11-13 04:59:55 UTC |
| Source: | https://github.com/cran/prioGene |
Title deal with network
deal_net(net, dise_gene)deal_net(net, dise_gene)
net |
a network |
dise_gene |
a matrix with one column of genes |
a matrix
deal_net(net,dise_gene)deal_net(net,dise_gene)
some genes
dise_genedise_gene
A matrix with 79 rows and 1 column
the first two columns are a net, and third column is their weight
edge_weightedge_weight
A matrix with 25 rows and 3 columns
the first column is the gene symbol, the second column is the go terms
genes_matgenes_mat
A matrix with 45 rows and 3 columns
the third column is the number of go terms
Title weight edge
get_edge_weight(net_disease_term, terms_mat)get_edge_weight(net_disease_term, terms_mat)
net_disease_term |
GO terms for each pair of nodes in the network |
terms_mat |
result of get_term_mat() |
a matrix
get_edge_weight(net_disease_term,terms_mat)get_edge_weight(net_disease_term,terms_mat)
Get a one-to-many matrix of gene and GO term
get_gene_mat(net_disease)get_gene_mat(net_disease)
net_disease |
a disease related network, matrix |
a matrix
get_gene_mat(net_disease)get_gene_mat(net_disease)
Title get neighbor of a node
get_neighbor(node, net)get_neighbor(node, net)
node |
a gene |
net |
a network |
a vector of gene
Title Get the GO terms for each pair of nodes in the network
get_net_disease_term(genes_mat, net_disease)get_net_disease_term(genes_mat, net_disease)
genes_mat |
a one-to-many matrix of GO term and gene |
net_disease |
a disease related network, matrix |
a matrix
get_net_disease_term(genes_mat,net_disease)get_net_disease_term(genes_mat,net_disease)
Title weight node
get_node_weight(genes_mat)get_node_weight(genes_mat)
genes_mat |
a one-to-many matrix of GO term and gene |
a matrix
get_node_weight(genes_mat)get_node_weight(genes_mat)
Title get the disease risk transition probability matrix
get_Q(node_weight, net_disease_term)get_Q(node_weight, net_disease_term)
node_weight |
a matrix, genes and their weights |
net_disease_term |
GO terms for each pair of nodes in the network |
a matrix
Title get the final genetic disease risk scores
get_R(node_weight, net_disease_term, bet, R_0, threshold = 10^(-9))get_R(node_weight, net_disease_term, bet, R_0, threshold = 10^(-9))
node_weight |
a matrix, genes and their weights |
net_disease_term |
GO terms for each pair of nodes in the network |
bet |
a parameter to measure the importance of genes and interactions |
R_0 |
the vector of initial disease risk scores for all genes |
threshold |
a threshold for terminating iterations |
a matrix
net_disease <- deal_net(net,dise_gene) genes_mat <- get_gene_mat(net_disease) node_weight <- get_node_weight(genes_mat) net_disease_term <- get_net_disease_term(genes_mat,net_disease) R_0<- get_R_0(dise_gene,node_weight,f=1) result <- get_R(node_weight, net_disease_term, bet = 0.5, R_0 = R_0, threshold = 10^(-9))net_disease <- deal_net(net,dise_gene) genes_mat <- get_gene_mat(net_disease) node_weight <- get_node_weight(genes_mat) net_disease_term <- get_net_disease_term(genes_mat,net_disease) R_0<- get_R_0(dise_gene,node_weight,f=1) result <- get_R(node_weight, net_disease_term, bet = 0.5, R_0 = R_0, threshold = 10^(-9))
Title get the vector of initial disease risk scores for all genes
get_R_0(disease_gene, node_weight, f = 1)get_R_0(disease_gene, node_weight, f = 1)
disease_gene |
a matrix of a column of genes |
node_weight |
a matrix, genes and their weights |
f |
an integer parameter to measure the significance of disease genes and candidate genes |
a vector
get_R_0(dise_gene,node_weight,1)get_R_0(dise_gene,node_weight,1)
Get a one-to-many matrix of GO term and gene
get_term_mat(net_disease)get_term_mat(net_disease)
net_disease |
a disease related network, matrix |
a matrix
get_term_mat(net_disease)get_term_mat(net_disease)
Title
get_W(node1, node2)get_W(node1, node2)
node1 |
a gene |
node2 |
a gene |
a number
a matrix, Human metabolic network
metabolic_netmetabolic_net
A matrix with 589,199 rows and 2 columns
a network of genes
netnet
A matrix with 2000 rows and 2 columns
a network of disease related genes
net_diseasenet_disease
A matrix with 26 rows and 2 columns
the first two columns is the network
net_disease_termnet_disease_term
A matrix with 25 rows and 4 columns
the third column is the go terms,the fourth column is the number of go terms
the fourth column is the number of go terms
a matrix, genes and their weights
node_weightnode_weight
A matrix with 45 rows and 2 columns
the vector of initial disease risk scores for all genes
R_0R_0
A vector of 45 number
a one-to-many matrix of GO term and gene
terms_matterms_mat
A matrix with 1172 rows and 3 columns