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. 2020 Sep 29;21:428. doi: 10.1186/s12859-020-03774-1

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© The Author(s) 2020

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 1 — Hypercluster workflow schematic. a Clustering algorithms and their respective hyperparameters are user-specified. Hypercluster then uses those combinations to create exhaustive configurations, and if selected a random subset is chosen. b Snakemake is then used to distribute each clustering calculation into different jobs. c Each set of clustering labels is then evaluated in a separate job by a user-specified list of metrics. d All clustering results and evaluation results are aggregated into tables. Best labels can also be chosen by a user-specified metric.