cluster_genes: cluster_genes
In OmicInt: Omics Network Exploration

Description Usage Arguments Value Examples

Function helps to select an optimal number of clusters and a model to be fitted during the EM phase of clustering for Gaussian Mixture Models. The function provides summaries and helps to visualise gene clusters based on generated data using score_genes function. Weighed gene expression is clustered based on the interactome complexity, i.e., the number of known interactors according to STRING DB, with a cutoff of 700 for the score threshold. The function also provides scatter plotting and dimension reduction plots to analyse the clusters and features in the experimental data.

1	cluster_genes(data, max_range = 20, clusters = NULL, modelNames = NULL)

`data`	data frame containing processed expression file from score_genes with LFCscore; class - data frame
`max_range`	number of clusters to consider during model selection; default 20 clusters; class - integer
`clusters`	number of clusters to test not based on the best BIC output, user also needs to supply modelNames; class - integer
`modelNames`	can only be supplied when clusters are also specified, this option will model based on the user parameters; class - string

A data frame object that contains a summary of clusters as well as clustering and summary plots

 ## Not run: 
path_to_test_data<- system.file("extdata", "test_scores.tabular", package="OmicInt")
# basic usage of cluster_genes
df<-utils::read.table(path_to_test_data)
df<-cluster_genes(df)
head(df)

## End(Not run)