highDimensionARI: Calculate ARI for high-dimensional data via data splits

In HTSCluster: Clustering High-Throughput Transcriptome Sequencing (HTS) Data

Calculate ARI for high-dimensional data via data splits

Description

This function is used to calculate Adjusted Rand Index (ARI) values for high-dimensional data.

Usage

highDimensionARI(x, y, splits = 2, verbose = FALSE) 

Arguments

x	Vector of classification labels
y	Vector of classification labels
splits	Number of subsets data should be split into
verbose	TRUE if verbose output is desired

Value

Value of Adjusted Rand Index for samples x and y

Author(s)

Andrea Rau

References

Rau, A., Maugis-Rabusseau, C., Martin-Magniette, M.-L., Celeux G. (2015). Co-expression analysis of high-throughput transcriptome sequencing data with Poisson mixture models. Bioinformatics, 31(9):1420-1427.

Rau, A., Celeux, G., Martin-Magniette, M.-L., Maugis-Rabusseau, C. (2011). Clustering high-throughput sequencing data with Poisson mixture models. Inria Research Report 7786. Available at https://inria.hal.science/inria-00638082.

HTSCluster documentation built on Sept. 8, 2023, 5:56 p.m.