Shimadzu Corporation

Other

Keio University

Teikyo University

Shimdaszu Corporation

Shigeki Kajihara

Sadamu Tomita

Yoshikazu Yoshida

Naokazu Yamaki

Masahiro Ikegami

Kiyoshi Ogawa

Mitsutoshi Setou

Yuichiro Fujita

Masaaki Matsuura

Masaru Nishiguchi

Osamu Furuhashi

Shoichi Okamura

Hideaki Izumi

Akira Arakawa

Shin Fujita

Tohru Kinugawa

Jingwen Yao

Keisuke Toyama

Ryota Aisu

Hiroko Morinaga

Matthew Kelly

Masaru Ushijima

Yusuke Tamai

Shiro Ikeda

Masatoshi Wakui

Shunichi Amari

To improve the reliability of mutual diagnosis in a cancer determination by machine learning, m/z values of ions originating from tumor markers or similar substances used in other related tests are stored in a particular m/z-value database. A spectrum information filtering section deletes signal intensities at the m/z values stored in the particular m/z-value database from a large number of mass spectra classified by the presence or absence of cancer. Using the data which remain after the deletion as training data, a training processor obtains training-result information and stores it in a training result database. A judgment processor similarly deletes signal intensities at the predetermined m/z values from mass spectrum data obtained for a target sample to be judged. Then, based on the training-result information stored in the training-result database, the judgment processor determines whether the target sample should be classified into a cancerous group or non-cancerous group.

Mass spectrometric data analysis device and analysis method

This analytical data analysis method uses machine learning of analysis result data () measured by an analyzer (), and includes generating simulated data () in which a data variation has been added to the analysis result data () within a range that does not affect identification, performing the machine learning using the generated simulated data (), and performing discrimination using a discrimination criterion () obtained through the machine learning.

Analytical data analysis method and analytical data analyzer

Provided is a method for sorting a number of samples into an appropriate number of clusters according to their characteristics. Highly-correlated peaks are extracted from mass spectrum data obtained for the samples (S). Using the extracted peaks, highly-correlated sample pairs are extracted (S). While removing samples having low degrees of correlation, highly-correlated sample pairs are integrated to form core clusters (S). Using singular peaks characterizing each core cluster, two or more core clusters are integrated to form clusters (S-S). These clusters include mixed clusters in which two or more clusters are mixed. Member determination formulae are created based on the singular peaks of each cluster (S-S). All samples, including those which have been excluded from the cluster determination process, are classified into clusters based on the member determination formulae (S). The member determination formulae can also be used to assign a new sample to one of the cluster.

Big-data analyzing Method and mass spectrometric system using the same method

When conducting imaging mass analysis for a region to be measured on a sample, an individual reference value calculating part obtains a maximum value in P/Iof respective measuring points, and stores the value together with measured data as an individual reference value. When performing comparison analysis for a plurality of the data obtained from different samples, a common reference value determining part reads out corresponding a plurality of the individual reference values and determines a minimum value as a common reference value Fmin. A normalization calculation processing part normalizes the respective intensity values by multiplying the intensity values read out from an external memory device by a normalization coefficient long_Max&#xd7;(Fmin/P) obtained from the common reference value Fmin, TIC values Pi at the respective measuring points, and a maximum allowable value long_Max of a variable storing the intensity values at the time of operation.

Analysis data processing method and device

If spatial measurement point intervals in imaging mass analysis data of two samples to be compared are different and the degrees of spatial distribution spreading of substances are compared, one of the data is defined as a reference, the measurement point intervals in the other of the data are redefined so as to be equalized to the reference, and a mass spectrum at each virtual measurement point set as a result of the redefinition is obtained through interpolation or extrapolation based on a mass spectrum at an actual measurement points. If the arrays of the m/z values of mass spectra are different for each sample, the m/z value positions of the mass spectrum in one of the data are defined as a reference, and the intensity values corresponding to the reference m/z values are obtained through interpolation or extrapolation for the mass spectrum of the other of the data.

Imaging mass analysis data processing method and imaging mass spectrometer

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.

Mass-analysis data processing method and system

Even when only mass spectra wherein the reproducibility of peak intensities is low are obtained in a mass spectrometry apparatus using, for example, a MALDI ion source, the correction of shifts in retention time using TICs for a plurality of specimens is performed with good precision. For each mass spectrum, variable scaling is executed which combines such first scaling as to equalize the extent of variations in signal intensity values in one mass spectrum, among different mass spectra, and second scaling for performing weighting according to relative variations in signal intensity values for each mass spectrum (S). The signal intensity values after the scaling are added to obtain a total signal intensity value for one measurement time point (S). From a plurality of total signal intensity values thus obtained, a TIC is created (S). Using these TICs, RT alignment is executed (S). Thus, the similarity in TIC waveforms increases, and RT alignment can be suitably performed.

Chromatograph mass spectrometry data processing apparatus

The present invention provides a method and apparatus for efficiently handling a large amount of data collected by an imaging mass analysis to present significant information for the analysis of the tissue structure of a biological sample or other objects in an intuitively understandable form for analysis operators. For each pixelon a sample, a mass-to-charge ratio m/z(i) corresponding to the maximum intensity MI(i) in a mass spectrum is listed, and the largest value MII of the maximum intensities of all the pixels are extracted. A color scale corresponding to the intensity values within a range of 0 to MII is defined. For each pixel, the maximum intensity MI is compared with the color scale to assign a color to that pixel. A mapping image with the pixels shown in the respective colors is created and displayed. Simultaneously, a spectrum showing the relationship between MI(i) and m/z(i) of all the pixels is created in such a manner that the peak colors correspond to the pixel colors on the mapping image. The mapping image shows the tissue structure of the sample. By comparing this image with the spectrum, the m/z of a noticeable substance in the sample can be identified.

Mass analysis data processing method and mass analysis data processing apparatus

In an imaging mass analysis, image information of a sample allows users to grasp specific information about the sample, such as distribution of a portion with a particular function or effect. The mass spectrum intensity data are normalized for each pixel so that the sum of the intensities over the entire mass-to-charge ratio (m/z) is one. Entropy is calculated by totaling the product of the intensity normalizing at each m/z and the logarithm of that intensity over the entire m/z range. After the entropy is calculated for each pixel, the pixels are colored according to their entropy values to display a two-dimensional color image of entropy distribution. The entropy of a cancerous portion is relatively small because of a high content of a specific kind of substance and the simplified composition of the substances. Thus, the cancerous part and the normal part of the entropy image can be distinguished.

Method and system for processing mass analysis data

(EN) MS analysis, MSanalysis, . . . , MSanalysis for peptide mixture are sequentially executed to obtain respective mass spectra (S). At this time, an analysis in which precursor ion is changed or a different cleavage condition is set for the same precursor ion is performed plural times to put together peaks appearing in mass spectra that are obtained respectively. After the number of peaks is increased, a useful peak is extracted using commonality and complementarity of the peaks of MSspectrum and MSspectrum and classification is performed for each type of peaks extracted to obtain an appearance frequency for each classification (S, S). An evaluation score on whether the extracted peak is a product ion and on a terminal is calculated based on reliability and appearance frequency that are obtained in advance (S). The evaluation score is used in estimating sequence with the use of the extracted peak to decide and output, for example, the priority of sequence candidates (S, S).

Mass spectrometry system

A method creates an accurate mass spectrum with a high resolving power based on a plurality of TOF spectra, while reducing the computation to assure real-time processing. TOF spectra are measured when ions are ejected from the loop orbit. Then a coincidence detection method determines what mass-to-charge ratio a peak appearing on the TOF spectra originates from. The time range in which a corresponding peak appears on other TOF spectra is set, and the existence of the peak in that range is determined. When the corresponding peak is found on other TOF spectra, the m/z is deduced from the peak on the TOF spectrum with the highest resolving power and a mass spectrum is created. From the peak density around the peak of interest, the reliability of the deduction is computed. For a low reliability peak, the ion ejection time is optimized and the TOF spectrum is measured again.

Mass analysis data processing method and mass spectrometer

The present invention aims at providing a method and apparatus for presenting, based on an enormous amount of data collected by an imaging mass analysis, information which is significant for understanding the tissue structure and other information of a biological sample and which is intuitively easy to understand to analysis operator. For each pixelon a sample, the mass-to-charge ratio m/z (i) corresponding to the maximum intensity MI(i) in the mass spectrum is extracted, and all the pixels are grouped into clusters in accordance with their m/z (i). One cluster corresponds to one substance. Then, the largest maximum intensity MI(i) among the maximum intensities of the pixels included in a cluster is extracted as the representative maximum intensity MI(cj) for each cluster, and these representative maximum intensities MI(cj) are displayed with cluster number cj. When an operator specifies one or more clusters to be displayed by reference to these MI(cj), different colors respectively are assigned to the specified clusters, and a cluster image in which the pixels included in each cluster are colored is created and displayed. On the cluster image, the spatial distributions of a plurality of substances are shown in different colors. Simultaneously, an integrated mass spectrum of all the pixels is displayed, in which the peaks corresponding to the selected clusters are colored in the same color as in the cluster image.

Method and apparatus for processing mass analysis data

An MS analysis is performed for each micro area within a specified mass analysis area on a sample. Based on the data obtained by this analysis, a distribution image of a specified m/z ratio of m/z range is created and displayed on a display screen (S-S). When an operator selects a substance of interest on the displayed image and indicates its m/z (S), one or more micro areas in which the MS spectrum intensity at the specified m/z is equal to or higher than a threshold are extracted, and an MS/MS analysis using the m/z of the substance of interest as the precursor is performed on the extracted micro areas (Sand S). An average MS/MS spectrum is calculated from the MS/MS spectrum data obtained for those micro areas (S), and the substance of interest is identified based on the information relating to the peaks appearing on the average MS/MS spectrum (S).

Mass spectrometer

In a time-of-flight spectrum obtained when the overtaking of ions of different kinds has occurred, mass-to-charge ratios M, M, and Mare computed with a predetermined conversion formula by using a plurality of assumed numbers of turns for one peak. Then, the flight times Tf, Tf, and Tffor an overtakingless measurement are computed by using an inverse conversion formula. If peaks respectively corresponding to the flight times Tf, Tf, and Tffor an overtakingless measurement exist on an overtakingless time-of-flight spectrum, their intensities i, i, and iare obtained. Then, the intensity Ia of the original peak is distributed to the mass-to-charge ratios M, M, and Min accordance with the intensity ratio. The same intensity distribution processing is performed for all or selected plural peaks. The intensities assigned to the same mass-to-charge ratio are integrated. A mass spectrum is created for each of a plurality of overtaking time-of-flight spectra obtained by changing the timing of deviation of ions from a loop orbit, and the plurality of mass spectra are displayed in a window of a display unit so that they can be compared. Thereby, the probability of missing an ion due to the ion deviation timing can be reduced.

Mass-analyzing method and mass spectrometer

A measurement is performed in a no-passing mode, in which ions having different masses are prevented from making a complete turn through a loop orbit, to obtain a time-of-flight spectrum without the passing of ions having different masses (Sand S). From the time of flight and other information of the peaks appearing on the time-of-flight spectrum (S), the number of turns and the time of flight in the loop-turn mode are predicted. Based on this prediction, a set of segments are defined on a time-of-flight spectrum in the loop-turn mode. The time widths of those segments are determined taking into account the spreads of the time widths of the aforementioned peaks. Since the number of turns is unique within each segment, the numbers of turns and the masses of the peaks can be uniquely determined as long as none of the segments overlap each other. Accordingly, it is determined whether there is any overlapped portion in the segments defined on the time-of-flight spectrum in the loop-turn mode under provisionally predetermined conditions. When a condition under which no overlapping occurs has been found, the segment setting is fixed (S-S). As a result, the timing for switching an ejection switch, which is used for ejecting ions from the loop orbit, is also determined. Based on this timing, a measurement in the loop-turn mode is performed (S).

Growing community of inventors

Uji, Japan

Shigeki Kajihara