xina_clustering {XINA} | R Documentation

## xina_clustering

### Description

Clustering multiplexed time-series omics data to find co-abundance profiles

### Usage

    xina_clustering(f_names, data_column, out_dir = getwd(),
      nClusters = 20, norm = "sum_normalization", chosen_model = "")

### Arguments

`f_names` | A vector containing input file (.csv) paths
`data_column` | A vector containing column names (1st row of the input file) of data matrix
`out_dir` | A directory path for saving clustering results. (default: out_dir=getwd())
`nClusters` | The number of desired maximum clusters
`norm` | Default is "sum_normalization". Sum-normalization is to divide the data matrix by row sum. If you want to know more about sum-normalization, see https://www.ncbi.nlm.nih.gov/pubmed/19861354. "zscore" is to calculate Z score for each protein. See scale.
`chosen_model` | You can choose a specific model rather than testing all the models that are available in mclust. mclustModelNames If you want k-means clustering instead of the model-based clustering, use "kmeans" here.

### Value

a plot containing a BIC plot in current working directory and a list containing below information:

Item | Description
clusters | XINA clustering results
aligned | XINA clustering results aligned by ID
data_column | Data matrix column names
out_dir | The directory path containing XINA results
nClusters | The number of clusters desired by user
max_cluster | The number of clusters optimized by BIC
chosen_model | The used covariance model for model-based clustering
optimal_BIC | BIC of the optimized covariance model
condition | Experimental conditions of the user input data
color_for_condition | Colors assigned to each experimental conditions which is used for condition composition plot
color_for_clusters | Colors assigned to each clusters which is used for XINA clustering plot
norm_method | Used normalization method

### Examples

    # Generate random multiplexed time-series data
    random_data_info <- make_random_xina_data()

    # Data files
    data_files <- paste(random_data_info$conditions, ".csv", sep='')

    # time points of the data matrix
    data_column <- random_data_info$time_points

    # mclust requires the fixed random seed to get reproduce the clustering results
    set.seed(0)

    # Run the model-based clustering to find co-abundance profiles
    example_clusters <- xina_clustering(data_files, data_column=data_column,
    nClusters=30)

    # Run k-means clustering to find co-abundance profiles
    example_clusters <- xina_clustering(data_files, data_column=data_column,
    nClusters=30,
    chosen_model="kmeans")

[Package XINA version 1.4.0 Index]