%0 Journal Article
%T A Normalization-Free and Nonparametric Method Sharpens Large-Scale Transcriptome Analysis and Reveals Common Gene Alteration Patterns in Cancers
%A Chang Sun
%A Cui-Ping Yang
%A Hai-Peng Li
%A Huan Wu
%A Jumin Zhou
%A Li-Ping Jiang
%A Qi-Gang Li
%A Qin Yu
%A Qing-Peng Kong
%A Qiu-Shuo Shen
%A Shao-Yan Pu
%A Song-Qing Fan
%A Xiangting Wang
%A Xiao-Qiong Chen
%A Xiao-Xiong Wang
%A Ying Li
%A Yong-Bin Chen
%A Yong-Han He
%J Theranostics
%D 2017
%I Ivyspring International Publisher
%R 10.7150/thno.19425
%X Heterogeneity in transcriptional data hampers the identification of differentially expressed genes (DEGs) and understanding of cancer, essentially because current methods rely on cross-sample normalization and/or distribution assumption¡ªboth sensitive to heterogeneous values. Here, we developed a new method, Cross-Value Association Analysis (CVAA), which overcomes the limitation and is more robust to heterogeneous data than the other methods. Applying CVAA to a more complex pan-cancer dataset containing 5,540 transcriptomes discovered numerous new DEGs and many previously rarely explored pathways/processes; some of them were validated, both in vitro and in vivo, to be crucial in tumorigenesis, e.g., alcohol metabolism (ADH1B), chromosome remodeling (NCAPH) and complement system (Adipsin). Together, we present a sharper tool to navigate large-scale expression data and gain new mechanistic insights into tumorigenesis.
%K Cross-Value Association Analysis
%K normalization-free
%K pan-cancer
%K transcriptome
%K heterogeneity.
%U http://www.thno.org/v07p2888.htm