In:
Bioinformatics, Oxford University Press (OUP), Vol. 37, No. 17 ( 2021-09-09), p. 2780-2781
Kurzfassung:
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. Availabilityand implementation Mercator is freely available at the Comprehensive R Archive Network (https://cran.r-project.org/web/packages/Mercator/index.html).
Materialart:
Online-Ressource
ISSN:
1367-4803
,
1367-4811
DOI:
10.1093/bioinformatics/btab037
Sprache:
Englisch
Verlag:
Oxford University Press (OUP)
Publikationsdatum:
2021
ZDB Id:
1468345-3
SSG:
12
