Multiple Criteria Optimization (MCO): A gene selection deterministic tool in RStudio

Abstract

Identifying genes with the largest expression changes (gene selection) to characterize a given condition is a popular first step to drive exploration into molecular mechanisms and is, therefore, paramount for therapeutic development. Reproducibility in the sciences makes it necessary to emphasize objectivity and systematic repeatability in biological and informatics analyses, including gene selection. With these two characteristics in mind, in previous works our research team has proposed using multiple criteria optimization (MCO) in gene selection to analyze microarray datasets. The result of this effort is the MCO algorithm, which selects genes with the largest expression changes without user manipulation of neither informatics nor statistical parameters. Furthermore, the user is not required to choose either a preference structure among multiple measures or a predetermined quantity of genes to be deemed significant a priori. This implies that using the same datasets and performance measures (PMs), the method will converge to the same set of selected differentially expressed genes (repeatability) despite who carries out the analysis (objectivity). The present work describes the development of an open-source tool in RStudio to enable both: (1) individual analysis of single datasets with two or three PMs and (2) meta-analysis with up to five microarray datasets, using one PM from each dataset. The capabilities afforded by the code include license-free portability and the possibility to carry out analyses via modest computer hardware, such as personal laptops. The code provides affordable, repeatable, and objective detection of differentially expressed genes from microarrays. It can be used to analyze other experiments with similar experimental comparative layouts, such as microRNA arrays and protein arrays, among others. As a demonstration of the capabilities of the code, the analysis of four publicly-available microarray datasets related to Parkinson´s Disease (PD) is presented here, treating each dataset individually or as a four-way meta-analysis. These MCO-supported analyses made it possible to identify MMP9 and TUBB2A as potential PD genetic biomarkers based on their persistent appearance across each of the case studies. A literature search confirmed the importance of these genes in PD and indeed as PD biomarkers, which evidences the code´s potential.

Assessment of the Degree of Coincidence between Differentially Expressed Genes in Pancreatic Cancer with and without CAR T Cell treatment

“…Selection of DEGs in this work was carried out through Multiple Criteria Optimization (MCO), (5,11,12). MCO can be used to identify DEGs from the analysis of single microarray datasets as well as from the meta-analysis of multiple microarray datasets.…”

“…In this way, MCO provides a way to detect DEGs from single or multiple array comparative experiments in an objective and repeatable manner. A more detailed explanation of MCO and its code can be found in (5).…”

“…Microarrays allow measuring the relative expression of thousands of genes simultaneously and the resulting datasets can subsequently be analyzed to identify DEGs. Multiple Criteria Optimization (MCO) was used to identify the DEGs in the experiments selected due to its capacity to preserve objectivity and repeatability (5). MCO can be accessed in the address (https://github.com/DeiverSuarez/OBAMA) as part of a proprietary larger tool suite called Optimization-based Analysis of Multiple Arrays (OBAMA).…”

Identification of hub biomarkers of myocardial infarction by single-cell sequencing, bioinformatics, and machine learning

“…According to the degree of the PPI network, the significance of intersection genes was acquired ( 18 ). The correlation among co-DEGs was evaluated using the Pearson’s correlation analysis, which was calculated using Rstudio (Version 1.4.1717) ( 19 ). This result was visualized using Cytoscape (Version 3.6.1) ( 20 ).…”

WOMDI-Apriori Data Mining Algorithm for Clustered Indicators Analysis of Specialty Groups in Higher Vocational Colleges

“…The original Apriori association rule mining algorithm and the improved WOMDI-Apriori association rule mining algorithm are applied to the input dataset by calling the "arules" function package in R language [22], and the mining results are summarized in Figure 3.…”