Nonnegative principal component analysis for mass spectral serum profiles and biomarker discovery

Abstract

Background: As a novel cancer diagnostic paradigm, mass spectroscopic serum proteomic pattern diagnostics was reported superior to the conventional serologic cancer biomarkers. However, its clinical use is not fully validated yet. An important factor to prevent this young technology to become a mainstream cancer diagnostic paradigm is that robustly identifying cancer molecular patterns from high-dimensional protein expression data is still a challenge in machine learning and oncology research. As a well-established dimension reduction technique, PCA is widely integrated in pattern recognition analysis to discover cancer molecular patterns. However, its global feature selection mechanism prevents it from capturing local features. This may lead to difficulty in achieving high-performance proteomic pattern discovery, because only features interpreting global data behavior are used to train a learning machine.

Methods: In this study, we develop a nonnegative principal component analysis algorithm and present a nonnegative principal component analysis based support vector machine algorithm with sparse coding to conduct a high-performance proteomic pattern classification. Moreover, we also propose a nonnegative principal component analysis based filter-wrapper biomarker capturing algorithm for mass spectral serum profiles.

Results: We demonstrate the superiority of the proposed algorithm by comparison with six peer algorithms on four benchmark datasets. Moreover, we illustrate that nonnegative principal component analysis can be effectively used to capture meaningful biomarkers.

Conclusion: Our analysis suggests that nonnegative principal component analysis effectively conduct local feature selection for mass spectral profiles and contribute to improving sensitivities and specificities in the following classification, and meaningful biomarker discovery.

Figure 1

Comparison on the five algorithm performance. Comparison on the five algorithm performance on four datasets: 'O1' (ovarian), 'O2' (ovarian-qaqc), 'L' (liver), and 'C' (colorectal). The NPCA-SVM algorithm demonstrated leading performance over the other four algorithms.

Figure 2

Visualization of the colorectal samples by using three biomarkers. The 48 control and 64 cancer samples are visualized by using the three biomarkers. Two types of samples demonstrated significantly different means and variations.

Figure 3

Visualization of the ovarian samples by using three biomarkers. The 253 ovarian samples are visualized by using the three biomarkers. The 91 control and 162 cancer samples are separated into two disjoint clusters.