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. 2021 Dec 16;13:189. doi: 10.1186/s13073-021-01000-y

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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/. 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 — Impact of intra-patient transcriptomic heterogeneity on clinical outcomes. a Scatterplots showing the correlation between transcriptomic heterogeneity estimates based on in silico deconvolution (ITTH score) versus single-cell analysis derived values (scITTH score). b Survival analysis with ITTH clusters (Low/Medium/High) identified significant differences across various cancer types (FDR-corrected log-rank p value<0.05). c Plots depicting the overlap between clusters based on transcriptomic profiles (TC) and ITTH scores. P values are based on Fisher’s exact test and indicate that the clusters are distinct for most cancer types. d Comparison between ITTH scores of patients from different RECIST classes for Doxorubicin, Carboplatin, and Leucovorin highlighting significant differences (^*FDR-corrected Wilcoxon p value<0.05)