Mercator: A pipeline for multi-method, unsupervised visualization and distance generation

Zachary B. Abrams, Caitlin E. Coombes, Suli Li, Kevin R. Coombes

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Unsupervised machine learning provides tools for researchers to uncover latent patterns in large-scale data, based on calculated distances between observations. Methods to visualize high-dimensional data based on these distances can elucidate subtypes and interactions within multi-dimensional and high-throughput data. However, researchers can select from a vast number of distance metrics and visualizations, each with their own strengths and weaknesses. The Mercator R package facilitates selection of a biologically meaningful distance from 10 metrics, together appropriate for binary, categorical and continuous data, and visualization with 5 standard and high-dimensional graphics tools. Mercator provides a user-friendly pipeline for informaticians or biologists to perform unsupervised analyses, from exploratory pattern recognition to production of publication-quality graphics.

Original languageEnglish
Pages (from-to)2780-2781
Number of pages2
JournalBioinformatics
Volume37
Issue number17
DOIs
StatePublished - Sep 1 2021

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