Comparison of Feature Selection Methods for Cross-Laboratory Microarray Analysis
Journal
IEEE/ACM Transactions on Computational Biology and Bioinformatics
Journal Volume
10
Journal Issue
3
Pages
593-604
Date Issued
2013
Author(s)
Liu H.-C.
Peng P.-C.
Hsieh T.-C.
Yeh T.-C.
Lin C.-J.
Chen C.-Y.
Hou J.-Y.
Shih L.-Y.
Abstract
The amount of gene expression data of microarray has grown exponentially. To apply them for extensive studies, integrated analysis of cross-laboratory (cross-lab) data becomes a trend, and thus, choosing an appropriate feature selection method is an essential issue. This paper focuses on feature selection for Affymetrix (Affy) microarray studies across different labs. We investigate four feature selection methods: (t)-test, significance analysis of microarrays (SAM), rank products (RP), and random forest (RF). The four methods are applied to acute lymphoblastic leukemia, acute myeloid leukemia, breast cancer, and lung cancer Affy data which consist of three cross-lab data sets each. We utilize a rank-based normalization method to reduce the bias from cross-lab data sets. Training on one data set or two combined data sets to test the remaining data set(s) are both considered. Balanced accuracy is used for prediction evaluation. This study provides comprehensive comparisons of the four feature selection methods in cross-lab microarray analysis. Results show that SAM has the best classification performance. RF also gets high classification accuracy, but it is not as stable as SAM. The most naive method is (t)-test, but its performance is the worst among the four methods. In this study, we further discuss the influence from the number of training samples, the number of selected genes, and the issue of unbalanced data sets. ? 2013 IEEE.
SDGs
Other Subjects
Decision trees; Diseases; Gene expression; Laboratories; Microarrays; Statistical tests; Acute lymphoblastic leukemia; cancer; Classification performance; Comprehensive comparisons; Feature selection methods; Laboratory experiments; Microarray data analysis; Significance Analysis of Microarrays; Feature extraction; DNA microarray; factual database; gene expression profiling; gene expression regulation; genetics; human; metabolism; neoplasm; procedures; statistical model; Databases, Factual; Gene Expression Profiling; Gene Expression Regulation, Neoplastic; Humans; Models, Statistical; Neoplasms; Oligonucleotide Array Sequence Analysis
Type
journal article