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. 2023 Apr 3;42(1):99–108. doi: 10.1038/s41587-023-01728-5

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© The Author(s) 2023

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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Extended Data Fig. 8 — (a) Heatmaps show the overview of the simulated genes with a dropout of 70% on both unspliced and spliced reads. We simulated raw mRNA counts using a Poisson distribution to obtain the unspliced and spliced abundances with 50%, 60%, and 70% technical zeros. (b) Scatter plot shows a high correlation between the simulated (background truth) and the predicted 𝛼/𝛽 (top) and 𝛼/𝛾 (bottom) under different dropout ratios of the spliced and unspliced reads. The dropout was applied to both spliced and unspliced reads. The Pearson correlation coefficients between the prediction and the simulation are computed. The Pearson correlation coefficient in data with different dropout rates is larger than 0.96 and 0.84 for α/β and α/γ, respectively. (c) The predicted 𝛼/𝛽 (top) and 𝛼/𝛾 (bottom) are plotted against numbers of cells on the simulated mono-kinetic, multi-lineage forward branching, transcriptional boost, and multi-lineage backward branching genes.