Abstract
In a recently published article in Genome Biology, Li and colleagues introduced TIMER, a gene expression deconvolution approach for studying tumor-infiltrating leukocytes (TILs) in 23 cancer types profiled by The Cancer Genome Atlas. Methods to characterize TIL biology are increasingly important, and the authors offer several arguments in favor of their strategy. Several of these claims warrant further discussion and highlight the critical importance of data normalization in gene expression deconvolution applications.Please see related Li et al correspondence: www.dx.doi.org/10.1186/s13059-017-1256-5 and Zheng correspondence: www.dx.doi.org/10.1186/s13059-017-1258-3
Highlights
In a recently published article in Genome Biology, Li and colleagues introduced TIMER, a gene expression deconvolution approach for studying tumor-infiltrating leukocytes (TILs) in 23 cancer types profiled by The Cancer Genome Atlas
The authors assert that CIBERSORT succumbs to statistical collinearity, leading to biased estimations
After examining the TIMER source code, we found that, unlike most previous deconvolution methods including CIBERSORT, TIMER solves the regression problem without normalizing inferred cell subset frequencies to 1
Summary
Chih Long Liu, Maximilian Diehn and Ash A.
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