Work

Data normalization considerations for digital tumor dissection

Year: 2017

Type: article

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.

Source: Genome biology

DOI: 10.1186/s13059-017-1257-4

Authors: Aaron M. Newman, Andrew J. Gentles, Chih Long Liu, Maximilian Diehn, Ash A. Alizadeh

Institutions: Stanford Medicine, Stanford Cancer Institute, Institute for Stem Cell Biology and Regenerative Medicine, Stanford University

Language: English

Topic: Gene expression and cancer classification

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $5,490