Feature Selection and Classification of MAQC-II Breast Cancer and Multiple Myeloma Microarray Gene Expression Data

Qingzhong Liu,Zhongxue Chen,Andrew H Sung,Xudong Huang,Jianzhong Liu,Youping Deng

doi:10.1371/journal.pone.0008250

Abstract

Microarray data has a high dimension of variables but available datasets usually have only a small number of samples, thereby making the study of such datasets interesting and challenging. In the task of analyzing microarray data for the purpose of, e.g., predicting gene-disease association, feature selection is very important because it provides a way to handle the high dimensionality by exploiting information redundancy induced by associations among genetic markers. Judicious feature selection in microarray data analysis can result in significant reduction of cost while maintaining or improving the classification or prediction accuracy of learning machines that are employed to sort out the datasets. In this paper, we propose a gene selection method called Recursive Feature Addition (RFA), which combines supervised learning and statistical similarity measures. We compare our method with the following gene selection methods: Support Vector Machine Recursive Feature Elimination (SVMRFE)Leave-One-Out Calculation Sequential Forward Selection (LOOCSFS)Gradient based Leave-one-out Gene Selection (GLGS) To evaluate the performance of these gene selection methods, we employ several popular learning classifiers on the MicroArray Quality Control phase II on predictive modeling (MAQC-II) breast cancer dataset and the MAQC-II multiple myeloma dataset. Experimental results show that gene selection is strictly paired with learning classifier. Overall, our approach outperforms other compared methods. The biological functional analysis based on the MAQC-II breast cancer dataset convinced us to apply our method for phenotype prediction. Additionally, learning classifiers also play important roles in the classification of microarray data and our experimental results indicate that the Nearest Mean Scale Classifier (NMSC) is a good choice due to its prediction reliability and its stability across the three performance measurements: Testing accuracy, MCC values, and AUC errors.

Highlights

Using microarray techniques, researchers can measure the expression levels for tens of thousands of genes in a single experiment
To exploit the information redundancy that exists among the huge number of variables and improve classification accuracy of microarray data, we propose a gene selection method, Recursive Feature Addition (RFA), which is based on supervised learning and similarity measures
In comparison to gradient based leaveone-out gene selection (GLGS), leave-one-out calculation sequential forward selection (LOOCSFS), and Support Vector Machine Recursive Feature Elimination (SVMRFE), RFA is associated with a greater number of currently known cancer related genes for prediction of pCR and a smaller number of currently known cancer related genes for prediction of erpos

Summary

Introduction

Researchers can measure the expression levels for tens of thousands of genes in a single experiment. This ability allows scientists to investigate the functional relationship between the cellular and physiological processes of biological organisms and genes at a genome-wide level. A high level analysis, such as gene selection, classification, or clustering, is applied to profile the gene expression patterns [1]. In the high-level analysis, partitioning genes into closely related groups across time and classifying patients into different health statuses based on selected gene signatures have become two main tracks of microarray data analysis in the past decade [2,3,4,5,6]. Designing feature selection methods that lead to reliable and accurate predictions by learning classifiers, is an issue of great theoretical as well as practical importance in high dimensional data analysis

Methods

Results

Conclusion