Analysis device

Abstract

The present invention relates to an analysis device that collects data and processes the data by performing predetermined analyses on samples respectively. The analysis device comprises: an analysis processing unit (23) that performs multivariate analysis processing based on the collected data in order to analyze the differences between multiple measurement objects or classify the multiple measurement objects; a feature extraction unit (24) that extracts characteristic parameters or elements that are presumed to be associated mainly with differences or classifications according to a predetermined criterion based on the multivariate analysis results; an image generation unit (25) that generates an imaging image of a predetermined two-dimensional range corresponding to the extracted parameters or elements; and a display processing unit (26) that assigns the same visual form to the characteristic parameters or elements extracted based on the multivariate analysis results and the imaging image generated corresponding thereto, and displays them on a display unit (4).

Description

Technical Field

[0001] The present invention relates to an analysis device that collects data by executing predetermined analysis on a sample or a subject, and performs multivariate analysis processing based on the collected data. Background Art

[0002] In recent years, multivariate analysis, one of the statistical analysis methods, has been actively used to analyze data obtained by various analysis devices such as mass analyzers, chromatographs, and optical analyzers. In particular, in the field of mass analysis imaging, mass spectrometry data showing signal intensities in a wide range of mass-to-charge ratios can be obtained at a very large number of points in space. Therefore, the amount of data is generally large, and multivariate analysis is a very useful method for extracting meaningful information from this large amount of data.

[0003] For example, in non-patent document 1, data analysis software having the following functions is disclosed: a reference image such as a staining image of a sample such as a biological tissue section is compared with an MS imaging image showing the signal intensity distribution at each mass-to-charge ratio m / z obtained by an imaging mass analyzer, and a mass-to-charge ratio showing a two-dimensional distribution similar to the reference image is extracted, and an MS imaging image at the extracted mass-to-charge ratio is displayed. As described in patent document 1, for example, in the search for similar images, a regression analysis based on the partial least squares method (PLS) can be used, with the data constituting the reference image as the target variable and the mass analysis imaging data as the explanatory variable.

[0004] In addition, the following operation is often performed: that is, if the user sets multiple regions of interest on a stained image (optical microscopic image) of a sample such as a biological tissue section, the mass spectrum data of each of the multiple regions of interest is analyzed using a method such as principal component analysis to explore the mass-to-charge ratio value, i.e., biomarker, that characterizes the difference in the regions of interest. In the principal component analysis, a score graph and a loading graph are generated, and each plotted point on the loading graph corresponds to a mass spectrum parameter, i.e., a mass-to-charge ratio value. Thus, a general user will perform such an operation: confirm the loading graph and select a mass-to-charge ratio that is presumed to be helpful for the difference in the regions of interest, and display a mass analysis imaging image of the mass-to-charge ratio.

[0005] Prior art literature

[0006] Patent Literature

[0007] Patent Document 1: International Publication No. 2017 / 002226

[0008] Patent Document 2: International Publication No. 2016 / 163385

[0009] Non-patent literature

[0010] Non-Patent Document 1: “Making data analysis of IMAGEREVEAL™ MS mass analysis imaging simple and as easy as you want”, [Online], Shimadzu Corporation, [retrieved on July 3, 2019], URL<URL:https: / / www.an.shimadzu.co.jp / bio / imagereveal / index.htm> Summary of the invention

[0011] Technical problem to be solved by the invention

[0012] However, in conventional imaging mass analyzers, for example, a user needs to determine which plot point on a loading diagram is characteristic for differential analysis and specify the plot point. Such a determination task not only places a burden on the user, but also requires a certain degree of experience and skill to perform the analysis. In addition, there is a problem that when a plurality of plot points that are presumed to be associated with different characteristics are specified on a loading diagram and MS imaging images of mass-to-charge ratios corresponding to these plot points are displayed, it is difficult to understand the relationship between the plurality of MS imaging images and which plot point on the loading diagram corresponds to.

[0013] Such a problem is not limited to imaging mass analysis devices, but also often occurs in analysis devices that perform other imaging analysis such as Raman spectroscopic imaging and infrared spectroscopic imaging. In addition, the same problem also exists in analysis devices that use the same mass analysis imaging method, such as the device described in Patent Document 1, which does not directly perform two-dimensional mass analysis on biological tissue slices, but arranges (sticks) sample micro-pieces collected from different measurement points on the sample on a sample plate in a manner that retains two-dimensional position information on the biological sample to prepare the sample, and sequentially performs mass analysis on the samples from the sample micro-pieces.

[0014] The present invention is completed to solve such technical problems, and its main purpose is to provide the user with useful information based on the multivariate analysis results in an easy-to-understand manner in an analysis device that implements multivariate analysis processing based on data obtained by performing a prescribed analysis on a sample, while eliminating the user's complicated operations and troublesome judgments that require experience, etc.

[0015] Solutions for solving the above technical problems

[0016] An analysis device according to one aspect of the present invention, which has been made to solve the above-mentioned technical problems, collects data by performing predetermined analysis on each sample and processes the data, and comprises:

[0017] an analysis processing unit that performs multivariate analysis processing based on the collected data in order to analyze differences between a plurality of measurement objects or classify the plurality of measurement objects;

[0018] a feature extraction unit that extracts characteristic parameters or elements that are estimated to be mainly associated with the difference or classification according to a predetermined criterion based on the result of the multivariate analysis process;

[0019] An image generating unit generating an image of a predetermined two-dimensional range corresponding to the parameter or element extracted by the feature extracting unit;

[0020] The display processing unit assigns the same visual form to the characteristic parameters or elements extracted from the multivariate analysis result and the corresponding imaging image generated by the image generation unit, and displays the multivariate analysis result and the imaging image on the display unit.

[0021] Effects of the Invention

[0022] The analysis device of the above scheme of the present invention is typically an imaging analysis device such as an imaging mass analyzer, a Raman spectroscopic imaging device, an infrared spectroscopic imaging device, etc. However, the imaging image is not limited to an image corresponding to a two-dimensional area on a sample, and can be applied to various analysis devices, for example, and is useful for the following operation: that is, performing multivariate analysis on data obtained by analyzing samples at the positions of grid points of a two-dimensional grid respectively placed on a sample plate, or data at the positions of grid points virtually arranged on such a two-dimensional grid, and generating an imaging image at a specific parameter value based on the result.

[0023] In addition, the multivariate analysis processing here can be set to include principal component analysis, partial least squares regression analysis, etc.

[0024] The "visual form" referred to here includes not only the display color but also the brightness of a specific color, the shade of a specific color, and the like.

[0025] The analysis device of the above scheme of the present invention is an imaging mass analysis device. When the multivariate analysis is principal component analysis, a loading diagram may be used as the result of the multivariate analysis process, and a feature extraction unit may extract characteristic plots that are presumed to be associated mainly with the difference or classification of the measurement object from the loading diagram. Furthermore, the image generation unit may generate an MS imaging image corresponding to one or more extracted plots, and the display processing unit may assign the same display color to the plots extracted on the loading diagram and the MS imaging image, and display the loading diagram and the MS imaging image on the display unit.

[0026] According to the analysis device of the above scheme of the present invention, in the analysis and classification of differences based on multivariate analysis, it is not necessary for the user to judge the parameters and elements that characterize the differences and classifications by himself, but it is possible to display the imaging images corresponding to the characteristic parameters and elements for confirmation. In addition, even if there are multiple features for differentiation and classification, the user can clearly understand which imaging image the parameters and elements corresponding to each feature are associated with on the display. In this way, it is possible to provide the user with useful information based on the multivariate analysis results in an easy-to-understand manner while saving the user's complicated operations and troublesome judgments that require experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a diagram showing the configuration of the main parts of an imaging mass spectrometer which is one embodiment of the analysis device of the present invention.

[0028] Figure 2 : is a flowchart showing the analysis procedure in the imaging mass spectrometer according to the present embodiment.

[0029] Figure 3 It is an explanatory diagram of the analysis process in the imaging mass spectrometer according to the present embodiment.

[0030] Figure 4 FIG. 1 is a schematic diagram showing measurement points where mass analysis is performed on a sample in the imaging mass spectrometer of the present embodiment.

[0031] Figure 5 This is a schematic diagram showing the correspondence between measurement points on a specimen and samples on a sample plate in an imaging mass spectrometer according to another embodiment.

[0032] Figure 6 This is a schematic diagram showing the origin of each sample on a sample plate in an analysis device according to still another embodiment. DETAILED DESCRIPTION

[0033] An imaging mass spectrometer as one embodiment of the analysis device of the present invention will be described with reference to the drawings.

[0034] [Configuration of the device of this embodiment]

[0035] Figure 1 It is a diagram showing the configuration of the main parts of the imaging mass spectrometer according to the present embodiment.

[0036] The device comprises: a measuring unit 1 , a data analyzing unit 2 , an input unit 3 and a display unit 4 , wherein the measuring unit 1 comprises an optical microscopic observation unit 11 and an imaging quality analysis unit 12 .

[0037] The optical microscopic observation section 11 obtains an optical microscopic image of the measurement area on the sample. On the other hand, the imaging mass analysis section 12 performs mass analysis over a specified mass-to-charge ratio range on a large number of measurement points (micro areas) set two-dimensionally in the measurement area on the same sample and collects mass spectrum data respectively. Here, the imaging mass analysis section 12 is a matrix-assisted laser desorption ionization (MALDI) ion trap time-of-flight mass analysis device, but the imaging mass analysis section 12 is not limited to this, and can also be a triple quadrupole mass analysis device equipped with a MALDI ion source, a quadrupole-time-of-flight mass analysis device, and a device using an LDI ion source other than a MALDI ion source, a SALDI ion source, etc. In addition, it can also be a device that cannot perform MS / MS analysis.

[0038] The data analysis unit 2 includes, as a functional module, a data storage unit 21, an optical microscopic image generating unit 22, a multivariate analysis processing unit 23, a characteristic parameter extraction unit 24, an MS imaging image generating unit 25, and an analysis result display processing unit 26, wherein the data storage unit 21 includes an optical microscopic image data storage unit 211 for storing optical microscopic image data and an MS imaging data storage unit 212 for storing mass spectrum data acquired by the imaging mass analysis unit 12.

[0039] In general, the data analysis unit 2 is implemented as a personal computer or a higher-performance computer, and the functions of each unit can be realized by executing dedicated analysis software installed in the computer.

[0040] [Characteristic Analysis Processing in the Device of the Present Embodiment]

[0041] Figure 2 This is a flowchart showing an example of the procedure of the analysis process in the imaging mass spectrometer according to the present embodiment. Figure 3 It is an explanatory diagram of the analysis process in the imaging mass spectrometer according to the present embodiment.

[0042] Reference Figure 1 and Figure 2 , Figure 3 , the characteristic analysis processing in the imaging mass analysis device of this embodiment is explained.

[0043] The user places a sample such as a biological sample slice in the measurement unit 1. The optical microscopic observation unit 11 receives the user's instruction from the input unit 3, takes an optical microscopic image of the sample, and saves the image data in the optical microscopic image data storage unit 211 of the data storage unit 21. The optical microscopic image generation unit 22 generates an optical microscopic image based on the data and displays it on the display unit 4. The user sets an appropriate measurement range on the optical microscopic image through the input unit 3, and then instructs the start of analysis after setting the analysis conditions. The analysis conditions are, for example, the pulse width of the ionization laser in the MALDI ion source, the laser power, the irradiation diameter, and the mass-to-charge ratio range of the measurement object.

[0044] like Figure 4 As shown, the imaging mass analysis unit 12 performs mass analysis point by point for a large number of measurement points (actually, microscopic areas of a size determined by the laser irradiation diameter, etc.) 102 defined in two dimensions within a specified measurement range 101 on the sample 100, and acquires mass spectrum data over a specified mass-to-charge ratio range. The mass spectrum data corresponding to each measurement point 102 thus collected is stored in the MS imaging data storage unit 212 of the data storage unit 21 (step S1).

[0045] Based on the mass spectrum data collected as described above (hereinafter, the mass spectrum data for all or part of the measurement points may be collectively referred to as MS imaging data), difference analysis between a plurality of regions of interest on a sample is performed as follows.

[0046] That is, when the user performs a predetermined operation through the input unit 3, the optical microscopic image generation unit 22 generates an optical microscopic image corresponding to the measurement range based on the data stored in the optical microscopic image data storage unit 211, and displays it on the display unit 4. The user appropriately sets a plurality of regions of interest (ROIs) on the image (step S2). Figure 3 (a) is an example of a state where a plurality of regions of interest are set on an optical microscopic image.

[0047] If the user instructs to perform a difference analysis based on principal component analysis after setting multiple regions of interest, the multivariate analysis processing unit 23 reads the mass spectrum data obtained from the measurement points included in the specified multiple regions of interest from the MS imaging data storage unit 212. Then, for each region of interest, the average mass spectrum or the maximum intensity mass spectrum is calculated based on the mass spectrum data obtained from the multiple measurement points included in one region of interest. Furthermore, peak detection is performed in the average mass spectrum or the maximum intensity mass spectrum, and the mass-to-charge ratio and signal intensity of each peak are calculated to generate a peak list. Then, the peak lists obtained in each region of interest are collected, and the number of the region of interest (for example, Figure 3The "ROI1" in (a) is taken as the vertical direction and the mass-to-charge ratio value is taken as the horizontal direction (of course, the vertical and horizontal directions can also be reversed), and a data matrix with signal intensity values ​​as elements is generated (step S3).

[0048] The multivariate analysis processing unit 23 performs principal component analysis on the generated data matrix, and generates a score diagram and a loading diagram as the principal component analysis result (step S4, refer to Figure 3 (b)). In principal component analysis, the number of principal components (the number of principal component axes) is generally determined during the analysis process. Thus, the number of score graphs and loading graphs corresponding to the number of principal components can be obtained.

[0049] Here, each plotted point on the score map corresponds to each row of the above data matrix, that is, each corresponds to a region of interest. Therefore, if the user sets 10 regions of interest, there will be 10 plotted points on the score map. Figure 3 In the example of (b), the first principal component axis PC1 and the second principal component axis PC2 appropriately reflect the differences in the regions of interest, the differences in the types and amounts of compounds generally present in the regions of interest, etc. Then, on the score graph, the plotted points corresponding to similar regions of interest are located at positions close to each other, and the plotted points corresponding to regions of interest with clear differences are located at separated positions.

[0050] On the other hand, the loading diagram is a diagram having the same two principal component axes as the score diagram, but each plotted point corresponds to each column of the above-mentioned data matrix, that is, the mass-to-charge ratio value as a parameter during analysis. The loading value indicates the degree of influence on the difference of the region of interest on the principal component axis. Thus, for example, the plotted point, i.e., the mass-to-charge ratio value, showing a large loading value on the first principal component axis PC1 has a relatively large influence on the difference of the region of interest on the first principal component axis PC1. In other words, the mass-to-charge ratio value showing a large loading value on the first principal component axis PC1 is likely to be used as a mark that can determine the degree of difference of the region of interest on the first principal component axis PC1.

[0051] Then, the characteristic parameter extraction unit 24 extracts the plot points (which are mass-to-charge ratio values ​​and peaks on the mass spectrum) that are inferred to represent the differences between the regions of interest based on the generated score map and the loading map (step S5). Specifically, for example, the principal component that contributes to the difference between the regions of interest is first selected from the relative distances of the plot points on the score map, and on this premise, in the loading map with the principal component as the axis, the plot points showing a larger load value on the axis can be selected. The number of plot points selected on the loading map can be predetermined, or, in the case of Figure 3 When the threshold values ​​PC1a and Pc2a can be set as shown in (b), an unlimited number of plot points can be selected based on the threshold values.

[0052] As described above, since the plotted points on the load diagram selected in step S4 correspond to the mass-to-charge ratio, the MS imaging image generation unit 25 reads the signal intensity values ​​of each measurement point corresponding to the selected mass-to-charge ratio from the MS imaging data storage unit 212, and generates an MS imaging image of the entire measurement range according to each mass-to-charge ratio (step S6). Here, it is also possible not to generate an MS imaging image of the entire measurement range, but to generate an MS imaging image for a narrower range specified by the user within the measurement range. The MS imaging image generated here is an image of a two-dimensional distribution of candidates for markers, i.e., compounds, which are more likely to be able to judge the degree of difference in the region of interest.

[0053] The analysis result display processing unit 26 generates a display screen in which the principal component analysis result and the plurality of MS imaging images are combined, and displays the display screen on the display unit 4. At this time, the same display color is assigned to the plurality of plot points selected as having the same tendency of features on the loading diagram as the principal component analysis result and the plurality of MS imaging images corresponding to the plot points (step S7).

[0054] For example, Figure 3 In the example, Figure 3 In the loading diagram shown in (b), three plot points that can be included as group A with respect to the first principal component axis PC1 are selected, and three plot points that can be included as group B with respect to the second principal component axis PC2 are selected. For example, red is assigned to the three plot points belonging to group A, and blue is assigned to the three plot points belonging to group B, and the above six plot points on the loading diagram obtained by plotting are assigned red or blue, respectively. On the other hand, the three MS imaging images corresponding to the three plot points belonging to group A are assigned the same red color as the three plot points, and a red frame-like frame is marked around the MS imaging images. In addition, the three MS imaging images corresponding to the three plot points belonging to group B are assigned the same blue color as the three plot points, and a blue frame-like frame is marked around the MS imaging images.

[0055] Through the above-described processing, it is possible to display the MS imaging image estimated to be characteristic in analyzing the difference of the region of interest designated by the user without the user's judgment and operation. In addition, since the plot points estimated to be significant for the difference analysis on the loading diagram and the MS imaging image are associated with the same display color, the user can understand at a glance the MS imaging images of a plurality of compounds estimated to have the same characteristics in the difference analysis with respect to a plurality of regions of interest, and can perform operations such as comparison.

[0056] When assigning a color to the MS imaged image and displaying the MS imaged image in the color, in addition to coloring the frame-shaped frame, various schemes can be used, such as labeling the color, setting the background color to the color, etc. That is, as long as the MS imaged image is easily visually confirmed to have been assigned the display color.

[0057] In addition, when the data constituting the MS imaging image as described above is saved as a data file, the color assigned to the MS imaging image can also be directly assigned to an icon showing the file or the folder in which the file is saved, so that when a list of files or folders is displayed, the specific file or folder can be easily distinguished from other files and identified.

[0058] In the above description, the difference analysis between multiple regions of interest on one sample is performed, but the multiple regions of interest may be replaced by multiple samples. That is, when the difference analysis between measurement ranges on different samples is performed, the above analysis method can be used as it is.

[0059] [Applications to multivariate analysis other than principal component analysis]

[0060] In the above description, principal component analysis is used as the multivariate analysis, but other multivariate analysis methods may also be used.

[0061] In the above-mentioned principal component analysis, a score value and a load value are obtained as a result of the multivariate analysis. For example, the load value is a numerical value associated with the representation of the difference between the regions of interest. Similarly, when partial least squares (PLS) regression analysis is used for the difference analysis between multiple regions of interest, the regression coefficient (regression coefficient matrix) can be obtained through the regression analysis, and it is estimated that the mass-to-charge ratio with a larger absolute value of the regression coefficient has a higher correlation with the difference between the regions of interest.

[0062] Therefore, similar to the relationship between the loading diagram and the MS imaging image described above, the regression coefficient that is estimated to have a high contribution to the representation of the difference between the regions of interest can be extracted from the regression coefficient matrix, the regression coefficient can be displayed in color, and the corresponding MS imaging image can be displayed by assigning the same color. In addition, a graph in the form of a spectrum showing the relationship between the mass-to-charge ratio and the regression coefficient can be generated based on the regression coefficient matrix, and the mass-to-charge ratio corresponding to the characteristic regression coefficient can be displayed in color on the graph.

[0063] [Other Modifications]

[0064] In addition, in the above embodiment, the same display color is assigned to the plotted points on the loading diagram and the MS imaging image corresponding thereto, but in addition to this, for example, when displaying a mass spectrum (including an average spectrum and a maximum intensity spectrum), the peak of the mass-to-charge ratio corresponding to the plotted point on the mass spectrum may be assigned the same color as the display color of the plotted point. This may not be the color of the peak itself but the color of a label showing the mass-to-charge ratio value of the peak, etc. Thus, it is possible to confirm at a glance which peak in the mass spectrum is a characteristic peak that is helpful for differentiation and classification.

[0065] In addition, the above-mentioned embodiment is an example of applying the present invention to an imaging mass spectrometer, but the present invention can be applied to various analysis devices other than imaging mass spectrometers, such as Raman spectroscopic imaging devices, infrared spectroscopic imaging devices, etc., which can visualize the distribution of substances (components) in a two-dimensional area on a sample.

[0066] In addition, as an analysis device that generates an image showing the distribution of substances (components) in a two-dimensional area on a sample, in addition to a device that directly analyzes the sample itself such as a biological tissue slice, there is also a known device based on the following method.

[0067] That is, Patent Document 2 discloses a method of combining a sample collection method called laser microdissection (LMD) with liquid chromatography mass spectrometry (LC / MS). In addition to the general LMD method of cutting a biological sample using a laser, there is also a sample collection method called hot melt LMD described in Patent Document 2.

[0068] For example, in the hot melt LMD method, a glass slide with a film that melts when heated is used, and the film is placed in close contact with the biological sample, and a near-infrared laser is irradiated to the target part of the biological sample. When the laser is irradiated, the film melts, and a part of the sample piece located at the target part (and its surroundings) is attached to the melted film and collected on the film.

[0069] like Figure 5 As shown, the sample pieces in the plurality of measurement points 102 in the two-dimensional region 101 on the biological sample 100 are collected at separate positions on the film 110, and the two-dimensional position information of the measurement point 102 on the biological sample is associated with the two-dimensional position information of the sample piece 111 on the film 110. Then, liquid samples are prepared respectively using each sample piece 111 collected on the film 110, and each liquid sample is analyzed in sequence using a liquid chromatography mass spectrometer. After performing mass analysis on all the liquid samples and acquiring mass spectrum data, the ion intensity information based on the mass spectrum data is reconstructed based on the two-dimensional position information assigned to each liquid sample, thereby generating a two-dimensional distribution image of the ion intensity corresponding to a specific component, i.e., an MS imaging image.

[0070] In addition, if Figure 5 As shown, each sample piece collected from each measurement point on the sample can be placed on a sample plate while maintaining their respective position information, and mass analysis can be performed on the sample piece on the sample plate using a mass analyzer equipped with a MALDI ion source to collect mass spectrum data.

[0071] As described above, instead of directly performing mass analysis on each measurement point on the sample, when performing mass analysis on a sample piece that is physically collected from each measurement point on the sample and transferred to another film or sample plate, an MS imaging image at a specific mass-to-charge ratio can be generated based on the mass spectrum data corresponding to each measurement point, which is the same as the device of the above-mentioned embodiment. Therefore, of course, the present invention can also be applied to such a method or device.

[0072] Furthermore, as described above, in the case where a large number of samples two-dimensionally arranged on a sample plate do not correspond to measurement points on a biological sample respectively, but are samples from different sources, the present invention can be applied as long as it is meaningful to display the two-dimensional distribution of ion intensity for each mass-to-charge ratio on the sample plate.

[0073] For example, for each of a plurality of samples, the eluate fractionated in each different elution time range by a liquid chromatography fractionation device is dripped onto a sample plate to prepare the sample, thereby obtaining the following: Figure 6 A sample plate 120 is prepared with a large number of samples 121 as shown. If mass spectrum data is collected by mass analysis of each sample on the sample plate, and an MS imaging image of a specific mass-to-charge ratio is generated based on the mass spectrum data, an image that can compare the amount of substance corresponding to its mass-to-charge ratio for each sample can be obtained. In this way, for example, when each sample on the sample plate is associated with a specific sample, thereby generating an MS imaging image at a specific mass-to-charge ratio of the entire sample plate, the present invention can also be applied.

[0074] The above-described embodiments and modifications are merely examples of the present invention, and appropriate modifications, corrections, additions, etc. made within the scope of the gist of the present invention other than those described above are obviously also included in the scope of the claims of the present application.

[0075] [Various options]

[0076] It is obvious to those skilled in the art that the above-mentioned exemplary embodiments are specific examples of the following aspects.

[0077] (Item 1) An analysis device according to one embodiment of the present invention is an analysis device that collects data by performing predetermined analysis on each sample and processes the data, and includes:

[0078] an analysis processing unit that performs multivariate analysis processing based on the collected data in order to analyze differences between a plurality of measurement objects or classify the plurality of measurement objects;

[0079] a feature extraction unit that extracts characteristic parameters or elements that are estimated to be mainly associated with the difference or classification according to a predetermined criterion based on the result of the multivariate analysis process;

[0080] An image generating unit generating an image of a predetermined two-dimensional range corresponding to the parameter or element extracted by the feature extracting unit;

[0081] The display processing unit assigns the same visual form to the characteristic parameters or elements extracted from the multivariate analysis result and the imaging image generated by the image generating unit corresponding thereto, and displays the multivariate analysis result and the imaging image on the display unit.

[0082] According to the analysis device described in Item 1, in the analysis and classification of differences based on multivariate analysis, it is not necessary for the user to judge the parameters and elements that characterize the differences and classifications, but it is possible to display the imaging images corresponding to the characteristic parameters and elements for confirmation. In addition, even when there are multiple features for differentiation and classification, the user can clearly understand which imaging image the parameters and elements corresponding to each feature are associated with on the display. Thus, it is possible to provide the user with useful information based on the multivariate analysis results in an easy-to-understand manner while saving the user from complicated operations and troublesome judgments that require experience.

[0083] (Item 2) It can be set that in the analysis device described in Item 1, the analysis device is an imaging mass analysis device, the parameter is a mass-to-charge ratio, the multivariate analysis process is a principal component analysis, and the multivariate analysis result is a loading diagram,

[0084] The feature extraction unit extracts characteristic plot points estimated to be mainly associated with the difference or classification from the loading map according to a predetermined criterion,

[0085] The image generation unit generates a mass analysis imaging image at a mass-to-charge ratio corresponding to the extracted plot point.

[0086] According to the analysis device described in item 2, when performing difference analysis between a plurality of samples or between a plurality of regions of interest on a single sample, for example, the same visual display form can be assigned to the plot points corresponding to the mass-to-charge ratio representing the difference and the MS imaging image at the mass-to-charge ratio on the loading diagram, and the loading diagram and the MS imaging image can be displayed on the display unit. Thus, the MS imaging image representing the difference can be confirmed without the user selecting the characteristic plot points on the loading diagram.

[0087] (Item 3) In the analysis device described in Item 1 or 2, the visual form can be a color on a display.

[0088] According to the analysis device described in Item 3, the user can confirm at a glance, for example, the plot point presumed to represent the difference on the loading diagram and the MS imaging image corresponding to the plot point.

[0089] Description of Reference Numerals

[0090] 1 Measurement Department

[0091] 11 Optical Microscope Observation Department

[0092] 12 Imaging Quality Analysis Department

[0093] 2 Data Analysis Department

[0094] 21 Data Storage

[0095] 211 Optical Microscope Image Data Storage Department

[0096] 212 MS imaging data storage unit

[0097] 22 Optical microscopic image generation unit

[0098] 23 Multivariate analysis processing unit

[0099] 24 Feature Parameter Extraction Unit

[0100] 25 MS imaging image generation unit

[0101] 26 Analysis result display processing unit

[0102] 3 Input section

[0103] 4 Display unit.

Claims

1. An imaging mass analysis device, which collects data by performing mass analysis on a sample and processes the data, characterized in that: have: an analysis processing unit that performs multivariate analysis processing based on the collected data in order to analyze differences between a plurality of measurement objects or classify the plurality of measurement objects; A feature extraction unit extracts characteristic parameters or elements that are inferred to be mainly associated with the difference or classification according to a predetermined criterion based on the result of the multivariate analysis process, wherein the parameter is a mass-to-charge ratio value and the element corresponds to the mass-to-charge ratio value; An image generating unit generating a mass analysis imaging image in a predetermined two-dimensional range corresponding to the parameter or element extracted by the feature extracting unit; The display processing unit assigns the same visual form to the characteristic parameters or elements extracted from the multivariate analysis result and the corresponding mass analysis imaging image generated by the image generation unit, and displays the multivariate analysis result and the mass analysis imaging image on the display unit.

2. The imaging mass analysis device according to claim 1, characterized in that: The multivariate analysis process is principal component analysis, and the multivariate analysis result is a loading diagram. The feature extraction unit extracts a plurality of characteristic plot points estimated to be mainly associated with the difference or classification from the loading map according to a predetermined criterion, The image generation unit generates a plurality of mass analysis imaging images at mass-to-charge ratio values ​​corresponding to each of the extracted plurality of plot points.

3. The imaging mass analysis device according to claim 1, characterized in that: The visual modality is the color on the display.

Images