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. 2020 Jun 17;10:9790. doi: 10.1038/s41598-020-66166-8

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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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

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Methodology proposed for analyzing links between cells. (a) Graph-DiffVAE uses an initial adjacency matrix and individual node features to predict more links between cells. (b) Projection of cells onto 2-dimensional t-SNE embedding of the latent node features learnt by Graph-DiffVAE and illustration of initial and predicted links between HSPCs and differentiated cells. (c) Adjacency matrix with input links between cells (the colour white indicates an input edge); each cell is connected to the highest positively correlated cell. (d) Adjacency matrix with predicted links between all cells by Graph-DiffVAE (the colour white indicates a predicted edge). (e) Co-expression matrix between all cells; each entry represents the absolute value of the Pearson correlation coefficient.