Feature Extraction Approach for Mass Spectrometry Imaging Data Using Non-negative Matrix Factorization

Mass spectrometry imaging (MSI) provides molecules composition information and corresponding spatial information on complex biological surfaces in a single experiment without label. It is a hotspot for getting significant amount of attention in the mass spectrometric community currently. However, the MSI data are large and complexity, which makes the reduction and feature extraction difficult. Some multivariate statistical analysis methods, for example, the famous principal component analysis (PCA), were developed to address this issue. But the results with negative value are hard to be interpreted as features about molecules. In this study, a feature extraction approach for MSI data by applying non-negative matrix factorization was developed. It could extract single molecules composition feature and corresponding distribution (basic images) feature, and further integrated the basic images to create a profile showing the whole sample by RGB (red, green and blue) color overlaid model clearly. The MSI data of a mouse brain section was used to test the efficiency of this approach. The white matter regions, the grey matter regions and the background regions were clearly observed and the corresponding molecules mass spectrums were extracted, which indicated that the approach was easier than PCA approach in results interpreting. Moreover, the MSI data of a human cancerous and adjacent normal bladder tissue sections on the same sample target were analyzed by the approach, and the cancerous regions and the normal regions were clearly differentiated. The software developed in this paper could be downloaded from the website http://www.msimaging.net.