similarity_score {GNET2} | R Documentation

## Compute the similarity from a predefined condition group

### Description

Compute the similarity between a predefined condition grouping and the sample cluster of each module, which is defined as the Adjusted Rand index between the two vectors, or the inverse of K-L divergence between the upper triangle matrix of the pairwise distance of predefined ranked condition grouping and the pairwise distance of sample cluster of each module.

### Usage

    similarity_score(gnet_result, group, ranked = FALSE)

### Arguments

`gnet_result` | Returned results from gnet().
`group` | predefined condition grouping
`ranked` | the grouping information is categorical(treatment/control) or ordinal(dosage, time points)?

### Value

A list of similarity scores between a predefined condition grouping and the sample cluster of each module, and the p values for the similarity scores based on permutation.

### Examples

    set.seed(1)
    init_group_num = 8
    init_method = 'kmeans'
    exp_data <- matrix(rnorm(50*10),50,10)
    reg_names <- paste0('TF',1:5)
    rownames(exp_data) <- c(reg_names,paste0('gene',1:(nrow(exp_data)-length(reg_names))))
    colnames(exp_data) <- paste0('condition_',1:ncol(exp_data))
    se <- SummarizedExperiment::SummarizedExperiment(assays=list(counts=exp_data))
    gnet_result <- gnet(se,reg_names,init_method,init_group_num)
    s <- similarity_score(gnet_result,rep(1:5,each = 2))

[Package GNET2 version 1.6.0 Index]