Nonnegative Principal Component Analysis for Cancer Molecular Pattern Discovery

Authors:
Xiaoxu Han
Affiliations:
Eastern Michgan University, Ypsilanti
Venue:
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Year:
2010

Citing 18
Cited 1

Nonlinear component analysis as a kernel eigenvalue problem

Neural Computation
Kernel independent component analysis

The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis

Kernel Methods for Pattern Analysis
Non-negative Matrix Factorization with Sparseness Constraints

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Systematic benchmarking of microarray data classification: assessing the role of non-linearity and dimensionality reduction

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Biomarker discovery in microarray gene expression data with Gaussian processes

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Nonsmooth Nonnegative Matrix Factorization (nsNMF)

IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving molecular cancer class discovery through sparse non-negative matrix factorization

Bioinformatics
Independent component analysis-based penalized discriminant method for tumor classification using gene expression data

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Projected Gradient Methods for Nonnegative Matrix Factorization

Neural Computation
Improved centroids estimation for the nearest shrunken centroid classifier

Bioinformatics
Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis

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Classification and feature selection algorithms for multi-class CGH data

Bioinformatics
A Direct Formulation for Sparse PCA Using Semidefinite Programming

SIAM Review
Convex and Semi-Nonnegative Matrix Factorizations

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast and robust fixed-point algorithms for independent component analysis

IEEE Transactions on Neural Networks
Algorithms for nonnegative independent component analysis

IEEE Transactions on Neural Networks
A "nonnegative PCA" algorithm for independent component analysis

IEEE Transactions on Neural Networks

Biomarker Identification and Cancer Classification Based on Microarray Data Using Laplace Naive Bayes Model with Mean Shrinkage

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)

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Abstract

As a well-established feature selection algorithm, principal component analysis (PCA) is often combined with the state-of-the-art classification algorithms to identify cancer molecular patterns in microarray data. However, the algorithm's global feature selection mechanism prevents it from effectively capturing the latent data structures in the high-dimensional data. In this study, we investigate the benefit of adding nonnegative constraints on PCA and develop a nonnegative principal component analysis algorithm (NPCA) to overcome the global nature of PCA. A novel classification algorithm NPCA-SVM is proposed for microarray data pattern discovery. We report strong classification results from the NPCA-SVM algorithm on five benchmark microarray data sets by direct comparison with other related algorithms. We have also proved mathematically and interpreted biologically that microarray data will inevitably encounter overfitting for an SVM/PCA-SVM learning machine under a Gaussian kernel. In addition, we demonstrate that nonnegative principal component analysis can be used to capture meaningful biomarkers effectively.