get_exp_cor_edges: get co-expression genes

In aib-group/PFP: Pathway Fingerprint Framework in R

Description

compute the correlation coefficient of gene expression data, return the most related genes

Usage

get_exp_cor_edges(
  gene_list,
  data_std,
  method = "spearman",
  num = 5,
  cor_threshold = NULL
)

Arguments

gene_list,	a vector of characters
data_std,	a matrix of data, such as gene expression data, whose rownames are gene names or ids and colnames are sample names
method,	a chareater, which method to compare the correlation of gene expression data it could be "pearson", "kendall", "spearman", "spearman" is default
num,	an integer, the top number of co-expressed genes to choose, 5 is default
cor_threshold,	a numeric, the threshold of the correlation coefficient to choose, default is NULL

Details

This function computes the correlation coefficient of gene expression data between gene_list and data_std, it will return a data.frame which can be translated a graph or network. In the data.frame, source refers to the genes in gene_list, target refers to the top coexpressed genes, weight refers to the correlated coefficient of genes in source and target, pathway is "uncertain" and edge_type is "coexp".Note, when choosing the top co-expressed genes, we will use the num param if the cor_threshold param is NULL. If not, we will choose the cor_threshold param.

Value

the coexp of edges.

Examples

data(data_std)
data(PFP_test1)
rank1 <- rank_PFP(object = PFP_test1,total_rank = TRUE)
pathway_select <- refnet_info(rank1)[1,"id"]
gene_test <- pathways_score(rank1)$genes_score[[pathway_select]]$ENTREZID
edges_coexp <- get_exp_cor_edges(gene_test,data_std)

aib-group/PFP documentation built on Dec. 27, 2020, 1:13 a.m.