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. 2023 Jun 30;25:111. doi: 10.1186/s13075-023-03089-5

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© The Author(s) 2023, corrected publication 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 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. 1 — Predicted frequency of immune cell types in whole blood. A Principal component (PC) plot of PC1 and PC2 for all patient samples. B Relative frequency of each of the 22 cell types predicted using CIBERSORTx in each sample. C The predicted frequency of the 11 selected cell types in the whole cohort. D Correlation matrix of the 11 cell types in the whole cohort. Spearman r coefficients are shown and the intensity of the colour indicates the strength of the correlation. E Pseudoheatmap of the 11 cell types according to exposure to GC, HCQ and immunosuppressants. The colour shows the magnitude of the Z score, *p < 0.05. HCQ, hydroxychloroquine; GC, glucocorticoids; AZA, azathioprine; MTX, methotrexate; MMF, mycophenolate mofetil; CYC, cyclophosphamide