Biology subjects

Coombes, C. E.

Publications and source records attributed to Coombes, C. E..

2 recordsLinked to original sources

bioRxiv2019

Mercator: An R Package for Visualization ofDistance Matrices

SummaryUnsupervised data analysis in many scientific disciplines is based on calculating distances between observations and finding ways to visualize those distances. These kinds of unsupervised analyses help researchers uncover patterns in large-scale data sets. However, researchers can select from a vast number of different distance metrics, each designed to highlight different aspects of different data types. There are also numerous visualization methods with their own strengths and weaknesses. To help researchers perform unsupervised analyses, we developed the Mercator R package. Mercator enables users to see important patterns in their data by generating multiple visualizations using different standard algorithms, making it particularly easy to compare and contrast the results arising from different metrics. By allowing users to select the distance metric that best fits their needs, Mercator helps researchers perform unsupervised analyses that use pattern identification through computation and visual inspection.\n\nAvailability and ImplementationMercator is freely available at the Comprehensive R Archive Network (https://cran.r-project.org/web/packages/Mercator/index.html)\n\nContactKevin.Coombes@osumc.edu\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2019

Malachite: A Gene Enrichment Meta-Analysis (GEM) Tool for ToppGene

BackgroundResearchers commonly use online tools such as ToppGene to conduct enrichment analyses on gene expression data. This process does not easily allow multiple gene data sets to be analyzed and compared at once. ToppGene requires the user to manually enter gene symbols or other gene identifiers into a text box and to manually sift through forms with many adjustable parameters in order to obtain a downloadable text file of results. This process makes the analysis of multiple sets of genes tedious, time-consuming, and error prone. To address this problem, we developed Malachite, a Python package that enables researchers to perform gene enrichment analyses on multiple gene lists and concatenate the resulting enrichment statistics. In this way, Malachite enables meta-enrichment analyses across multiple data sets.\n\nResultsTo illustrate its use, we applied Malachite to three data sets from the Gene Expression Omnibus comparing gene expression in the large airways of smokers and non-smokers. Biological processes enriched in all three data sets were related to xenobiotic stimulus; molecular functions typically involved nicotinamide adenine dinucleotide phosphate (NADP) activity.\n\nConclusionMalachite enables researchers to automate gene enrichment metaanalyses using ToppGene. Malachite also enhances ToppGenes gene set analysis of drug-gene relationships by further filtering for FDA approved drugs.

bioinformatics↗