Differential expression analysis for individual cancer samples based on robust within-sample relative gene expression orderings across multiple profiling platforms

• Published in: Oncotarget 2016-10, Vol.7 (42), p.68909-68920
• Main Authors: Guan, Qingzhou, Chen, Rou, Yan, Haidan, Cai, Hao, Guo, You, Li, Mengyao, Li, Xiangyu, Tong, Mengsha, Ao, Lu, Li, Hongdong, Hong, Guini, Guo, Zheng
• Format: Article
• Language: English
• Subjects: Algorithms; Computational Biology; Gene Expression Profiling; Gene Expression Regulation, Neoplastic; Humans; Lung - metabolism; Lung Neoplasms - genetics; Models, Statistical; Neoplasms - genetics; Oligonucleotide Array Sequence Analysis - methods; Reproducibility of Results; Research Paper
• ISSN: 1949-2553; 1949-2553
• DOI: 10.18632/oncotarget.11996

The highly stable within-sample relative expression orderings (REOs) of gene pairs in a particular type of human normal tissue are widely reversed in the cancer condition. Based on this finding, we have recently proposed an algorithm named RankComp to detect differentially expressed genes (DEGs) for individual disease samples measured by a particular platform. In this paper, with 461 normal lung tissue samples separately measured by four commonly used platforms, we demonstrated that tens of millions of gene pairs with significantly stable REOs in normal lung tissue can be consistently detected in samples measured by different platforms. However, about 20% of stable REOs commonly detected by two different platforms (e.g., Affymetrix and Illumina platforms) showed inconsistent REO patterns due to the differences in probe design principles. Based on the significantly stable REOs (FDR