Investigating the Efficacy of Nonlinear Dimensionality Reduction Schemes in Classifying Gene and Protein Expression Studies

• Published in: IEEE/ACM transactions on computational biology and bioinformatics Vol. 5; no. 3; pp. 368 - 384
• Main Authors: Lee, George, Rodriguez, Carlos, Madabhushi, Anant
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2008; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; and association rules; Availability; Bioinformatics; Bioinformatics (genome or protein) databases; Biomedical measurements; Cancer; classification; Clustering; Data and knowledge visualization; Data Interpretation, Statistical; Data mining; Decision trees; Discriminant analysis; Diseases; Explosions; Feature extraction or construction; Gene Expression Profiling - methods; Machine learning; Nonlinear Dynamics; Pattern Recognition, Automated - methods; Principal component analysis; Procurement; Protein engineering; Reproducibility of Results; Sensitivity and Specificity; Software; Studies; Bioinformatics (genome or protein) databases; and association rules; Feature extraction or construction; Data and knowledge visualization; Clustering; classification; Data mining
• ISSN: 1545-5963; 1557-9964; 2374-0043; 1557-9964
• DOI: 10.1109/TCBB.2008.36

The recent explosion in procurement and availability of high-dimensional gene- and protein-expression profile datasets for cancer diagnostics has necessitated the development of sophisticated machine learning tools with which to analyze them. A major limitation in the ability to accurate classify these high-dimensional datasets stems from the 'curse of dimensionality', occurring in situations where the number of genes or peptides significantly exceeds the total number of patient samples. Previous attempts at dealing with this issue have mostly centered on the use of a dimensionality reduction (DR) scheme, Principal Component Analysis (PCA), to obtain a low-dimensional projection of the high-dimensional data. However, linear PCA and other linear DR methods, which rely on Euclidean distances to estimate object similarity, do not account for the inherent underlying nonlinear structure associated with most biomedical data. The motivation behind this work is to identify the appropriate DR methods for analysis of high-dimensional gene- and protein-expression studies. Towards this end, we empirically and rigorously compare three nonlinear (Isomap, Locally Linear Embedding, Laplacian Eigenmaps) and three linear DR schemes (PCA, Linear Discriminant Analysis, Multidimensional Scaling) with the intent of determining a reduced subspace representation in which the individual object classes are more easily discriminable.