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. 2021 Feb 22;11:4338. doi: 10.1038/s41598-021-83767-z

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© The Author(s) 2021

Open AccessThis 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/.

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Immune pattern of ccRCC. Sub-figure (A) (created using PowerPoint v16.44) represents the algorithm of digital cytometry and clustering applied on TCGA data. Sub-figures (B) and (C), respectively show the estimated percentage of each immune cell by mass cytometry analysis of 73 ccRCC patients done by Chevrier et al.¹⁴ (B) and digital cytometry on 526 TCGA ccRCC tumors (C). Sub-figures (D) and (E) indicate the correlation map of estimated immune cell frequencies in 73 ccRCC tumors (D) and TCGA ccRCC tumors (E), respectively. Sub-figures (F) and (G) show the cluster heat map of immune cell frequencies in 73 ccRCC tumors (F) and TCGA ccRCC tumors (G). Sub-figures (H) and (I) respectively show a box plot format of the immune cell percentages in 73 ccRCC tumors (H) and TCGA ccRCC tumors (I). Sub-figure (J) shows 4 distinct immune patterns of ccRCC tumors obtained by K-mean clustering of cell frequencies of TCGA ccRCC tumors. Sub-figures (B)–(I) have been created using TumorDecon¹⁹ (https://pypi.org/project/TumorDecon/).