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. 2021 May 20;22:158. doi: 10.1186/s13059-021-02373-4

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© The Author(s) 2021

Open Access This 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 articles Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the articles 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. 6 — Predicting single-cell gene expression effects of unseen drugs using MichiGAN. a UMAP plots of sci-Plex dataset colored by cell type (left) and treatment (right). b UMAP plots of the predicted (green), real (blue) and control (red) cells for 6 predictions of three missing cell type/drug combinations (A549-S1628, K562-S1096, and MCF7-S7259). c Random forest errors between MichiGAN and -TCVAE for all combinations. MichiGAN was trained using mean representations (left) or representations sampled from the posterior distribution (right)