Abstract: Provided is a technique for using an optical microscope image of an area on a sample to collect area-specific information characterizing each kind of biological tissue from imaging mass analysis data. On an optical image of a two-dimensional target area on a sample, a difference is examined in the kind of tissue or other features and areas are specified, each regarded as the same kind of tissue. When data processing is initiated, peak information is extracted, for each specified area, from mass spectrum data of all the measurement points. A peak method is applied to each area to extract peak information. Then, when a command to compare a set of areas is given, the peak information of those areas is collected. By comparing the peak information of different areas by a machine learning algorithm or similar judging technique, area-specific peak information is obtained, and this information is stored in memory.

Abstract: Provided is a technique for using an optical microscope image of an area on a sample to collect area-specific information characterizing each kind of biological tissue from imaging mass analysis data. On an optical image of a two-dimensional target area on a sample, a difference is examined in the kind of tissue or other features and areas are specified, each regarded as the same kind of tissue. When data processing is initiated, peak information is extracted, for each specified area, from mass spectrum data of all the measurement points. A peak method is applied to each area to extract peak information. Then, when a command to compare a set of areas is given, the peak information of those areas is collected. By comparing the peak information of different areas by a machine learning algorithm or similar judging technique, area-specific peak information is obtained, and this information is stored in memory.

Abstract: In a clustering apparatus comprising an input unit (1) supplied with a dataset including a plurality of samples, a data processing unit (4) for processing the samples to classify each sample into a class, and an output unit (3) for producing a processing result representative of classification, a parameter memory (51) in a memory unit (5) memorizes a target parameter obtained from past experiment. A parameter estimating section (24) of the data processing unit estimates a clustering parameter by the use of the target parameter memorized in the parameter memory. An unidentifiable sample detecting section (25) of the data processing unit detects a sample as an unidentifiable sample if posterior probabilities calculated for the sample by a probability density function produced by the clustering parameter estimated by the parameter estimating section are smaller than a predetermined value.