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. 2021 Aug 9;13:126. doi: 10.1186/s13073-021-00932-9

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© The Author(s) 2021

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 — Decision tree model and its performance metrics on modified analysis of BWA-aligned EGA genomes. a Decision tree generated on the training dataset (n = 940). Node #0 at the top of the tree is the root node. Each node lists an STR tool (feature). The “samples” number represents the total number of genotype calls in a particular node, and “value” shows the number of expanded (or full-mutation, FM) and non-expanded (non-FM) genotypes. Gini index shows the impurity at each node. The terminal nodes or leaves with a Gini value of 0 have genotypes belonging entirely to either the expanded or non-expanded class. EHv3, ExpansionHunter version 3; wCtrls, analysis performed with controls. b Classification report summarizing the performance metrics of the model on test data (n = 236). Macro and weighted average (avg) show the unweighted and weighted mean of performance metrics calculated for Expanded and Not_Expanded class labels, respectively. c Receiver operating characteristics and precision-recall curves. d Confusion matrix showing the number of predicted and true labels on x- and y-axis, respectively. e Feature importance plot showing the STR tool on x-axis and the tool’s normalized (Gini) importance on y-axis