maigesPack: A Computational Environment for Microarray Data Analysis

TL;DR

maigesPack is an R package designed to streamline, standardize, and enhance the analysis of microarray gene expression data, addressing variability and complexity in experimental procedures.

Contribution

It introduces a comprehensive R package that consolidates various analysis methods to improve microarray data analysis's robustness and reproducibility.

Findings

Facilitates data organization and analysis

Integrates multiple analysis procedures

Enhances reproducibility of results

Abstract

Microarray technology is still an important way to assess gene expression in molecular biology, mainly because it measures expression profiles for thousands of genes simultaneously, what makes this technology a good option for some studies focused on systems biology. One of its main problem is complexity of experimental procedure, presenting several sources of variability, hindering statistical modeling. So far, there is no standard protocol for generation and evaluation of microarray data. To mitigate the analysis process this paper presents an R package, named maigesPack, that helps with data organization. Besides that, it makes data analysis process more robust, reliable and reproducible. Also, maigesPack aggregates several data analysis procedures reported in literature, for instance: cluster analysis, differential expression, supervised classifiers, relevance networks and functional classification of gene groups or gene networks.