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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. 5 — Marker genes for the mouse brain cell sub-types from the Inhibitory neuron broad types, and human foetal organ of origin classification. The mean logarithm of gene expression for mouse brain cell Inhibitory neuron cell sub-type markers. A the markers for scGeneFit; B the markers for RankCorr; C markers from SMaSH’s network mode. Particularly in the case of SMaSH unique patterns can still be identified in this highly granular cell-type identification problem, whereas approaches such as scGeneFit are not able to identify many markers which uniquely resolve the sub-types present. D SMaSH is able to select statistically significant markers for a highly imbalanced problem of distinguishing organs of origin in foetal scRNA-seq