Meta-analysis of cancer gene-profiling data.
DNA microarray profiles are plagued by the issue of large number of variables but small number of samples and are often notorious for their low signal-to-noise ratio for clinical applications. Therefore, a great need for meta-analysis techniques is emerging to yield more valid and informative results than each experiment separately. By exploring the power of several studies in one single analysis, meta-analysis of many cancer gene-profiling data increases the statistical power to detect differentially expressed genes and allows assessment of heterogeneity. OrderedList is such a method that was specially proposed for cancer gene expression data meta-analysis. It is superior to other methods in that it does not rely on strong effects of differential gene expression in a single study but on consistent regulated genes across multiple studies. This chapter introduces the R implementation of this methodology on real data sets to identify biomarkers for adenocarcinoma lung cancer.
Yang
Xinan
X
Division of Bioinformatics, State Key Laboratory of Bioelectronics (Chien-Shiung Wu Laboratory), Southeast University, Nanjing, China. xnyang@seu.edu.cn
Sun
Xiao
X
D000230:Adenocarcinoma / Q000235:genetics* / Q000378:metabolism; D000465:Algorithms; D014408:Biomarkers, Tumor / Q000378:metabolism*; D045744:Cell Line, Tumor; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D008175:Lung Neoplasms / Q000235:genetics* / Q000378:metabolism; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D008967:Molecular Biology / Q000379:methods; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated; D012984:Software
D014408:Biomarkers, Tumor