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. 2022 Oct 31;23(12):1777–1787. doi: 10.1038/s41590-022-01329-5

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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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Fig. 1 — a, Flowchart describing the collection, curation, standardization and preprocessing steps leading to the creation of the vaccine transcriptomics compendium. b, Histogram of the time points before (days −7 and 0) and after (days > 0) vaccination available in the Immune Signatures Data Resource. In the plot, each vaccine is represented by a different color, while the size of the bar is proportional to the number of samples with available transcriptomic data. Only adults aged 18–50 years, with available pre-vaccination data were included in the resource. c, Principal variance component analysis was used to estimate the proportion of the variance observed in the transcriptomic data that can be attributed to clinical (age, sex, ethnicity) and experimental variables (time after vaccination, vaccine). The proportion of the variance that could not be explained by those variables is depicted by the residuals (resid). Confidence intervals (95%, percentile method) and bar height (mean) were computed from 4,000 bootstrap replicates.