Minerva and minepy: a C engine for the MINE suite and its R, Python and MATLAB wrappers

TL;DR

This paper presents Minerva and minepy, efficient C implementations of the MINE algorithms with interfaces for R, Python, and MATLAB, optimized for large datasets and bioinformatics applications.

Contribution

It introduces a new C-based implementation of MINE algorithms that reduces memory usage and improves scalability and integration in bioinformatics workflows.

Findings

Reduces memory footprint compared to Java implementation.

Demonstrates scalability on large microarray and RNA-seq datasets.

Provides native parallelization for R interface.

Abstract

We introduce a novel implementation in ANSI C of the MINE family of algorithms for computing maximal information-based measures of dependence between two variables in large datasets, with the aim of a low memory footprint and ease of integration within bioinformatics pipelines. We provide the libraries minerva (with the R interface) and minepy for Python, MATLAB, Octave and C++. The C solution reduces the large memory requirement of the original Java implementation, has good upscaling properties, and offers a native parallelization for the R interface. Low memory requirements are demonstrated on the MINE benchmarks as well as on large (n=1340) microarray and Illumina GAII RNA-seq transcriptomics datasets. Availability and Implementation: Source code and binaries are freely available for download under GPL3 licence at http://minepy.sourceforge.net for minepy and through the CRAN repository http://cran.r-project.org for the R package minerva. All software is multiplatform (MS Windows, Linux and OSX).