Artificial Neural Networks Method of Classification and Identification for Mass Spectrometry Imaging Data of Biological Tissue

Mass spectrometry imaging(MSI),the combination of molecular mass analysis and spatial information,provides visualization of molecules on complex biological surfaces,thus is currently getting a significant amount of attention in the mass spectrometric community.One important problem in this researching field is how to develop an effective method of classification and identification for MSI data,especial for identifying the cancerous tissue from adjacent normal tissue and classifying the different functional regions in a complex biological tissue.For this purpose,we developed a new method,containing image reconstruction from raw mass spectral data,MSI data pre-processing,classification of tissue regions from background regions by self-organizing feature map and identification of special interesting regions from the whole tissue regions by learning vector quantization.The MSI data of six pairs(12 tissue samples) of human cancerous and adjacent normal bladder tissue samples were used to test the effect of this method.The result showed an error rate of less than 23.38% for identification of cancerous regions and an error rate of less than 9.08% for identification of the adjacent normal regions.The method was also tested to classify white matter and gray matter regions of three adjacent slices of mouse brain tissue.The slice in the middle was used to train and to establish an identification model;the other two slices were used to test the model.The inconsistent rate of the identification results by using self-organizing feature map is less than 4% comparing with the results using learning vector quantization.This indicated that the method could be performed simply and efficiently,to extend the capability of MSI,and underline its potential to be a regular tool applied to study on clinical application.