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. 2022 Aug 8;23:328. doi: 10.1186/s12859-022-04860-2

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© The Author(s) 2022

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. 2 — Classifying broad cell types based on SMaSH-specific marker genes. A Confusion matrices for the top 30 marker genes per cell type in the lung broad cell classification data-set for scGeneFit, RankCorr, SMaSH using the network mode, and SMaSH using the ensemble mode (using XGBoost). B Cell misclassification and $F_{1}$ scores for the two SMaSH modes against scGeneFit and RankCorr. C Benchmarking different SMaSH ensemble learning models across biological scRNA-seq data and related modalities