Soft Computing

, Volume 21, Issue 22, pp 6895–6906 | Cite as

Multistage feature selection approach for high-dimensional cancer data

  • Alhasan Alkuhlani
  • Mohammad Nassef
  • Ibrahim Farag
Methodologies and Application

Abstract

Cancer is a serious disease that causes death worldwide. DNA methylation (DNAm) is an epigenetic mechanism, which controls the regulation of gene expression and is useful in early detection of cancer. The challenge with DNA methylation microarray datasets is the huge number of CpG sites compared to the number of samples. Recent research efforts attempted to reduce this high dimensionality by different feature selection techniques. This article proposes a multistage feature selection approach to select the optimal CpG sites from three different DNAm cancer datasets (breast, colon and lung). The proposed approach combines three different filter feature selection methods including Fisher Criterion, t-test and Area Under ROC Curve. In addition, as a wrapper feature selection, we apply genetic algorithms with Support Vector Machine Recursive Feature Elimination (SVM-RFE) as its fitness function, and SVM as its evaluator. Using the Incremental Feature Selection (IFS) strategy, subsets of 24, 13 and 27 optimal CpG sites are selected for the breast, colon and lung cancer datasets, respectively. By applying fivefold cross-validation on the training datasets, these subsets of optimal CpG sites showed perfect classification accuracies of 100, 100 and 97.67%, respectively. Moreover, the testing of the three independent cancer datasets by these final subsets resulted in accuracies 96.02, 98.81 and 94.51%, respectively. The experimental results demonstrated high classification performance and small optimal feature subsets. Consequently, the biological significance of the genes corresponding to these feature subsets is validated using enrichment analysis.

Keywords

DNA methylation (DNAm) CpG sites Feature selection Genetic algorithms Support vector machine (SVM) Incremental feature selection (IFS) Enrichment analysis 

Supplementary material

500_2016_2439_MOESM1_ESM.pdf (221 kb)
Supplementary 1: The accuracy (Ac), sensitivity (Sn), specificity (Sp) of each run of IFS for each of the three training sets using MSFS (NoGA-Mode) (PDF 221 kb).
500_2016_2439_MOESM2_ESM.pdf (206 kb)
Supplementary 2: The accuracy (Ac), sensitivity (Sn), specificity (Sp) of each run of IFS for each of the three training sets using MSFS (GA-Mode) (PDF 206 kb).
500_2016_2439_MOESM3_ESM.pdf (261 kb)
Supplementary 3: The accuracy (Ac), sensitivity (Sn), specificity (Sp) of each run of IFS for each of the three independent sets using MSFS (NoGA-Mode) (PDF 260 kb).
500_2016_2439_MOESM4_ESM.pdf (262 kb)
Supplementary 4: The accuracy (Ac), sensitivity (Sn), specificity (Sp) of each run of IFS for each of the three independent sets using MSFS (GA-Mode) (PDF 261 kb).
500_2016_2439_MOESM5_ESM.pdf (241 kb)
Supplementary 5: GO terms and KEGG pathways of genes corresponding to the selected CpG sites for the three attempted cancer datasets (PDF 241 kb).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Alhasan Alkuhlani
    • 1
  • Mohammad Nassef
    • 1
  • Ibrahim Farag
    • 1
  1. 1.Department of Computer Science, Faculty of Computers and InformationCairo UniversityGizaEgypt

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