Newman Aaron M, Gentles Andrew J, Liu Chih Long, Diehn Maximilian, Alizadeh Ash A
Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, California, 94305, USA.
Division of Oncology, Department of Medicine, Stanford Cancer Institute, Stanford University, Stanford, California, 94305, USA.
Genome Biol. 2017 Jul 5;18(1):128. doi: 10.1186/s13059-017-1257-4.
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.
在最近发表于《基因组生物学》的一篇文章中，李及其同事介绍了TIMER，这是一种用于研究肿瘤浸润白细胞（TILs）的基因表达反卷积方法，所涉及的23种癌症类型由癌症基因组图谱进行了特征分析。表征TIL生物学特性的方法愈发重要，作者提出了几个支持其策略的论据。其中一些说法值得进一步探讨，并突出了数据归一化在基因表达反卷积应用中的至关重要性。请参阅相关的李等人的通信：www.dx.doi.org/10.1186/s13059-017-1256-5以及郑的通信：www.dx.doi.org/10.1186/s13059-017-1258-3。
