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 getting significant amount of attention in the mass spectrometric community currently.However,due to the large mount and complexity of MSI data,its data reduction and feature extraction are always a problem.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.A feature extraction approach for MSI data by applying non-negative matrix factorization was developed.It could extract single molecules composition feature and the corresponding distribution(basic images),and further integrated the basic images to create a profile showing the whole sample by RGB(red-green-blue) color overlaid model clearly.The MSI data of a mouse brain section were used to test the efficiency of this approach compared with PCA.The white matter regions,the grey matter regions and the background regions were clearly shown and the corresponding molecules mass spectra were extracted,which indicated the approach is easier than PCA in result 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,the cancerous regions and the normal regions were clearly differentiated.The software developed in this paper could be downcoaded from the website http://www.msimaging.net.