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. 2020 Nov 18;21(Suppl 10):614. doi: 10.1186/s12864-020-07009-8

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© The Author(s) 2020

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 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. 3 — Pseudotime trajectory estimation (a) Top. Each circled letter represents a cell in a cluster. Letters highlighted in black represent the connecting cells that show low variation of information to cells in the adjacent cluster. Cells in clusters are unordered. Bottom. Cells arranged in different orders within rectangles represent the cell orderings (sequence) within each cluster based on the variation of information between the connecting cells and the rest of the cells in the cluster. Refer to the main text for more explanation. (b) Heatmap of log(TPM+1) expression values of bivalent and bimodal genes from H9 time-series data. Cells (columns) are arranged according to the pseudotime ordering and colored according to the experimental timepoints. Genes (rows) are ordered using hierarchical clustering