基于非负分解方法的质谱成像数据特征提取

质谱成像技术能够在同一个实验里无需标记手段而获得样品表面的分子信息及其分布信息,是当前质谱分析的热点.其分析所得数据量大且复杂,使其特征难以提取.多元统计分析方法,特别是主成分分析法已应用于质谱成像数据的压缩和特征提取.然而由于主成分分析常产生负的数据结果,其意义难以解释且不易分解为单一的特征.本研究开发出一种基于非负分解的质谱成像数据提取方法,能够提取单一的分子特征及其在样品上的分布特征,并将多个单一的特征分布通过红、绿、蓝三色叠加显示,获得轮廓直观的综合特征分布.应用本方法对小鼠脑组织切片质谱成像数据进行分析,可直观分解出灰质区域、白质区域和背景区域,相对主成分分析方法更直观且易于解释.应用本方法对在同一个样品靶上的人膀胱癌变组织和其相邻非癌变组织切片质谱成像数据进行分析,癌变与非癌变组织间差异清晰直观.本研究设计的质谱成像软件可由http://www.msimaging.net获取.

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.