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. 2022 Dec 31;20(2):229–238. doi: 10.1038/s41592-022-01687-w

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© The Author(s) 2022

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 — RSF: real-valued spatial factorization, NSF: nonnegative spatial factorization, NSFH: NSF hybrid model, dim: number of latent dimensions or components, gau: Gaussian, poi: Poisson, nb: negative binomial. (a) Goodness of fit increases (training deviance decreases) for increasing number of IPs in spatial models RSF and NSF with larger numbers of components. (b) No clear effect of number of IPs on predictive accuracy (validation deviance). (c) Higher numbers of IPs are more computationally expensive (time to convergence).