1. Community assessment of methods to deconvolve cellular composition from bulk gene expression.
- Author
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White BS, de Reyniès A, Newman AM, Waterfall JJ, Lamb A, Petitprez F, Lin Y, Yu R, Guerrero-Gimenez ME, Domanskyi S, Monaco G, Chung V, Banerjee J, Derrick D, Valdeolivas A, Li H, Xiao X, Wang S, Zheng F, Yang W, Catania CA, Lang BJ, Bertus TJ, Piermarocchi C, Caruso FP, Ceccarelli M, Yu T, Guo X, Bletz J, Coller J, Maecker H, Duault C, Shokoohi V, Patel S, Liliental JE, Simon S, Saez-Rodriguez J, Heiser LM, Guinney J, and Gentles AJ
- Subjects
- Humans, Gene Expression Profiling methods, Transcriptome, Deep Learning, Computational Biology methods, Lymphocytes, Tumor-Infiltrating immunology, Gene Expression Regulation, Neoplastic, CD8-Positive T-Lymphocytes metabolism, CD4-Positive T-Lymphocytes metabolism, Neoplasms genetics, Neoplasms immunology, Neoplasms pathology
- Abstract
We evaluate deconvolution methods, which infer levels of immune infiltration from bulk expression of tumor samples, through a community-wide DREAM Challenge. We assess six published and 22 community-contributed methods using in vitro and in silico transcriptional profiles of admixed cancer and healthy immune cells. Several published methods predict most cell types well, though they either were not trained to evaluate all functional CD8+ T cell states or do so with low accuracy. Several community-contributed methods address this gap, including a deep learning-based approach, whose strong performance establishes the applicability of this paradigm to deconvolution. Despite being developed largely using immune cells from healthy tissues, deconvolution methods predict levels of tumor-derived immune cells well. Our admixed and purified transcriptional profiles will be a valuable resource for developing deconvolution methods, including in response to common challenges we observe across methods, such as sensitive identification of functional CD4+ T cell states., (© 2024. The Author(s).)
- Published
- 2024
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