Open AccessIdentification of suitable genes contributes to lung adenocarcinoma clustering by multiple meta‐analysis methods
Author(s) -
Yang ZeHui,
Zheng Rui,
Gao Yuan,
Zhang Qiang
Publication year - 2016
Publication title -
the clinical respiratory journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.789
H-Index - 33
eISSN - 1752-699X
pISSN - 1752-6981
DOI - 10.1111/crj.12271
Subject(s) - meta analysis , bioconductor , microarray analysis techniques , computational biology , microarray , cluster analysis , gene , hierarchical clustering , gene expression profiling , preprocessor , medicine , bioinformatics , data mining , biology , genetics , gene expression , computer science , artificial intelligence , pathology
Background With the widespread application of high‐throughput technology, numerous meta‐analysis methods have been proposed for differential expression profiling across multiple studies. Objectives We identified the suitable differentially expressed ( DE ) genes that contributed to lung adenocarcinoma ( ADC ) clustering based on seven popular multiple meta‐analysis methods. Methods Seven microarray expression profiles of ADC and normal controls were extracted from the A rray E xpress database. The Bioconductor was used to perform the data preliminary preprocessing. Then, DE genes across multiple studies were identified. Hierarchical clustering was applied to compare the classification performance for microarray data samples. The classification efficiency was compared based on accuracy, sensitivity and specificity. Results Across seven datasets, 573 ADC cases and 222 normal controls were collected. After filtering out unexpressed and noninformative genes, 3688 genes were remained for further analysis. The classification efficiency analysis showed that DE genes identified by sum of ranks method separated ADC from normal controls with the best accuracy, sensitivity and specificity of 0.953, 0.969 and 0.932, respectively. The gene set with the highest classification accuracy mainly participated in the regulation of response to external stimulus ( P = 7.97E‐04), cyclic nucleotide‐mediated signaling ( P = 0.01), regulation of cell morphogenesis ( P = 0.01) and regulation of cell proliferation ( P = 0.01). Conclusions Evaluation of DE genes identified by different meta‐analysis methods in classification efficiency provided a new perspective to the choice of the suitable method in a given application. Varying meta‐analysis methods always present varying abilities, so synthetic consideration should be taken when providing meta‐analysis methods for particular research.