A Normalization-Free and Nonparametric Method Sharpens Large-Scale Transcriptome Analysis and Reveals Common Gene Alteration Patterns in Cancers

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—both sensitive to heterogeneous values. Here, we developed a ne...

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Detalles Bibliográficos
Autores principales: Li, Qi-Gang, He, Yong-Han, Wu, Huan, Yang, Cui-Ping, Pu, Shao-Yan, Fan, Song-Qing, Jiang, Li-Ping, Shen, Qiu-Shuo, Wang, Xiao-Xiong, Chen, Xiao-Qiong, Yu, Qin, Li, Ying, Sun, Chang, Wang, Xiangting, Zhou, Jumin, Li, Hai-Peng, Chen, Yong-Bin, Kong, Qing-Peng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Ivyspring International Publisher 2017
Materias:
Research Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5562223/
https://www.ncbi.nlm.nih.gov/pubmed/28824723
http://dx.doi.org/10.7150/thno.19425

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5562223/
https://www.ncbi.nlm.nih.gov/pubmed/28824723
http://dx.doi.org/10.7150/thno.19425

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