WO2014112567A1 - Appareil pour la classification de groupes cellulaires et procédé de classification de groupes cellulaires - Google Patents

Appareil pour la classification de groupes cellulaires et procédé de classification de groupes cellulaires Download PDF

Info

Publication number
WO2014112567A1
WO2014112567A1 PCT/JP2014/050720 JP2014050720W WO2014112567A1 WO 2014112567 A1 WO2014112567 A1 WO 2014112567A1 JP 2014050720 W JP2014050720 W JP 2014050720W WO 2014112567 A1 WO2014112567 A1 WO 2014112567A1
Authority
WO
WIPO (PCT)
Prior art keywords
data
cell group
population
group
age
Prior art date
Application number
PCT/JP2014/050720
Other languages
English (en)
Japanese (ja)
Inventor
真也 黒田
大樹 浅野
新介 宇田
峰夫 黒川
裕 矢冨
Original Assignee
国立大学法人 東京大学
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 国立大学法人 東京大学 filed Critical 国立大学法人 東京大学
Publication of WO2014112567A1 publication Critical patent/WO2014112567A1/fr

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/69—Microscopic objects, e.g. biological cells or cellular parts
    • G06V20/698—Matching; Classification
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N2015/1006—Investigating individual particles for cytology
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N15/14—Optical investigation techniques, e.g. flow cytometry
    • G01N2015/1402—Data analysis by thresholding or gating operations performed on the acquired signals or stored data
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N15/14—Optical investigation techniques, e.g. flow cytometry
    • G01N2015/1477—Multiparameters
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N15/14—Optical investigation techniques, e.g. flow cytometry
    • G01N2015/1488—Methods for deciding

Definitions

  • the present invention relates to a cell group classification apparatus and a cell group classification method for classifying a cell group to be processed.
  • Non-patent Document 1 analysis using flow cytometry (FCM) has been performed for myelodysplastic syndrome (MDS), and a prognosis prediction model based on this analysis has been proposed.
  • the present invention has been made in view of the above circumstances, and an object thereof is to provide a cell group classification device and a cell group classification method capable of performing analysis in consideration of a realistic pathological condition.
  • the present invention for solving the problems of the above-mentioned conventional example is a cell group classification device, a holding means for holding population data obtained based on measurement data of a cell group sample diagnosed as normal, and processing Based on the measurement data obtained for the target cell group, means for generating processing data that can be tested for similarity to the population data, the generated processing data, and a mother held in the holding means
  • a test means for testing the similarity with the population data a classification means for classifying the cell group to be processed into one of a plurality of predetermined groups according to a predetermined standard based on the result of the test, Is to be included.
  • the holding means is provided for each predetermined age group provided by a provider of an age belonging to each age group, and is obtained based on measurement data of cell group samples diagnosed as normal. Holding the population data, the testing means tests the similarity between the population data for each age group corresponding to the age group to which the age of the provider of the cell group to be processed belongs and the processing data It is good as well.
  • the measurement data is a plurality of parameters obtained by flow cytometry, and the population data and the processing data are m-dimensional (m is a natural number) distribution data obtained based on the parameters. Good.
  • the cell group classification method includes a step of acquiring population data based on measurement data of a cell group sample diagnosed as normal, and based on measurement data obtained for a cell group to be processed. , Generating process data that can be tested for similarity to the population data, testing the similarity between the generated process data and the population data held in the holding means, and In accordance with a predetermined standard based on the result, the cell group to be processed is classified into one of a plurality of predetermined groups.
  • the cell group classification apparatus 1 includes a control unit 11, a storage unit 12, an operation unit 13, an output unit 14, and an analysis information input unit 15 as illustrated in FIG. , Connected to the flow cytometry instrument 2.
  • the cell group classification apparatus 1 executes the cell group classification method according to the embodiment of the present invention.
  • control unit 11 is a program control device such as a CPU (Central Processing Unit), and operates according to a program stored in the storage unit 12.
  • the control unit 11 performs processing using population data obtained based on measurement data of cell group samples diagnosed as normal, which are stored in a storage unit 12 described later.
  • the control unit 11 generates processing data that can be tested for similarity to the population data based on the measurement data obtained for the cell group to be processed, and the generated processing data and the population data Test for similarity.
  • the control part 11 classify
  • the storage unit 12 includes a memory and a disk device.
  • the storage unit 12 stores population data obtained based on measurement data of a cell group sample diagnosed as normal (a sample of a cell group of the same tissue as the cell group to be processed).
  • the measurement data is a plurality of parameters obtained by applying a cell group collected from a specimen diagnosed as normal to a flow cytometry device, and the population data is based on the parameters. This is m-dimensional (m is a natural number) distribution data obtained.
  • this population data is obtained by flow cytometry for each cell group obtained from a plurality of specimens diagnosed as normal, at least FSC (forward scattered light), SSC ( Side-scattered light) and measurement data (information indicating fluorescence intensity) of a given cell surface marker (plural types) were obtained, accumulated, and the population density distribution function was estimated for the accumulated result.
  • the density distribution function is estimated by adaptive partitioning method, kernel density estimation (Parzen E. (1962). On estimation of a probability density function and mode, Ann. Math.athStat. 33, pp. 1065-1076.)
  • kernel density estimation Parzen E. (1962). On estimation of a probability density function and mode, Ann. Math.athStat. 33, pp. 1065-1076.
  • the population data is, for example, the result of an estimation calculation of these density distribution functions, and this estimation calculation method can be used widely in various statistical calculation software. Is omitted.
  • FSC forward scattered light
  • SSC side scattered light
  • predetermined cells are obtained by flow cytometry for each cell group obtained from a plurality of specimens diagnosed as normal.
  • Measurement data information indicating fluorescence intensity
  • the first two-dimensional distribution data with the X axis as FSC and the Y axis as SSC, and the second two dimensional with the X axis as FSC and the Y axis as measurement data for the cell surface marker Generation of distribution data and third two-dimensional distribution data with the X axis as SSC and the Y axis as cell surface marker measurement data, and a density distribution function for each of these two-dimensional distribution data, such as a kernel density estimation method, etc.
  • the result is stored in the storage unit 12 as population data. That is, when r types of cell surface markers are measured, 3r two-dimensional distribution data is generated, and 3r population data is stored in the storage unit 12.
  • the storage unit 12 stores information on test results (known result information) for each cell group included in a plurality of cell groups whose disease progress is known in advance. This known result information will be described later.
  • the storage unit 12 holds a program executed by the control unit 11.
  • the program may be provided by being stored in a computer-readable recording medium such as a DVD-ROM and stored in the storage unit 12.
  • the storage unit 12 also operates as a work memory for the control unit 11.
  • the operation unit 13 includes a keyboard and a mouse.
  • the operation unit 13 accepts an operation from the user and outputs information representing the content of the operation to the control unit 11.
  • the output unit 14 is a display, a printer, or other output device, and outputs information according to an instruction input from the control unit 11.
  • the analysis information input unit 15 is an interface connected to the flow cytometry device 2 and outputs measurement data input from the flow cytometry device 2 to the control unit 11.
  • the control unit 11 of the present embodiment executes a program stored in the storage unit 12, and functionally, as shown in FIG. 2, a processing data generation unit 21, a population data acquisition unit 22, and a test
  • the unit 23, the classification unit 24, and the result presentation unit 25 are configured.
  • the processing data generation unit 21 acquires measurement data obtained for the cell group to be processed from the flow cytometry device 2 via the analysis information input unit 15. Then, the processing data generation unit 21 generates processing data that can be tested for similarity with the population data stored in the storage unit 12. Specifically, this processing data can be generated as follows. Based on the measurement data (a plurality of parameters) obtained by the flow cytometry device 2, m-dimensional distribution data is generated.
  • the distribution data is, for example, 2 + r pieces of measurement data of predetermined r types of cell surface markers that are the same as the source of at least FSC (forward scattered light), SSC (side scattered light), and population data. At least (2 + r) -dimensional distribution data including information.
  • the processing data generation unit 21 sets the X axis as FSC and the Y axis as SSC for each of the r types of cell surface markers based on this distribution data.
  • First two-dimensional distribution data second two-dimensional distribution data in which the X-axis is FSC, the Y-axis is cell surface marker measurement data, the X-axis is SSC, and the Y-axis is cell surface marker measurement data.
  • 3 two-dimensional distribution data is estimated for each of the 3r two-dimensional distribution data, and the result is output to the test unit 23 as processing data.
  • various methods such as an adaptive partitioning method and a kernel density estimation can be used in the same manner as used when obtaining population data.
  • the population data acquisition unit 22 reads population data corresponding to each of the first to third two-dimensional distribution data relating to each of the r types of cell surface markers stored in the storage unit 12.
  • the data is output to the test unit 23.
  • the test unit 23 includes two-dimensional distribution data of processing data (hereinafter, this processing data is written as dp) input from the processing data generation unit 21 and population data (hereinafter referred to as population data acquisition unit 22). , This population data is written as de), and the similarity to the corresponding two-dimensional distribution data is tested. Specifically, the test unit 23 performs the distribution data dp (x1, x2,... Xm) and de (x1, x2,...
  • Each likelihood ratio statistic ⁇ n_1, ⁇ n_2, ⁇ n_3 for each distribution data is calculated.
  • likelihood ratio statistics ⁇ n_1, ⁇ n_2, and ⁇ n_3 are each asymptotic to a chi-square distribution with k-1 degrees of freedom when the sample size is large, and the absolute value of this value increases as the distribution differs.
  • the test unit 23 performs likelihood ratio statistics ⁇ n_1 and ⁇ n_2 for each of the two-dimensional distribution data corresponding to each of the first to third two-dimensional distribution data for each of the r types of cell surface markers. , ⁇ n_3 is obtained.
  • the test unit 23 accumulates the likelihood ratio statistics ⁇ n_1, ⁇ n_2, ⁇ n_3 for each two-dimensional distribution data obtained for each of the r types of cell surface markers to obtain r pieces of r for each of the r types of cell surface markers.
  • a likelihood ratio statistic ⁇ n is obtained.
  • the known result information stored in the storage unit 12 will be described.
  • the known result information associates information (I) indicating the progress of the disease and likelihood ratio statistic ⁇ n for a plurality of cell groups in which the progress of the disease is known in advance. Is.
  • this likelihood ratio statistic ⁇ n is obtained for each of the r types of cell surface markers.
  • the classification unit 24 performs processing using this known result information.
  • the classification unit 24 classifies the cell group to be processed into one of a plurality of predetermined groups according to a predetermined standard based on the calculation result in the test unit 23. Specifically, the classification unit 24 calculates the likelihood ratio statistic ⁇ n for each of the r types of cell surface markers generated based on the processing data, and the r types of cell surface markers for each of a plurality of cell groups included in the known result information. Each likelihood ratio statistic ⁇ n is used as two r-dimensional vectors having the likelihood ratio statistic ⁇ n as elements, and clustering processing is performed. As the clustering processing method, various widely known processes such as a longest distance method using a distance between r-dimensional vectors and a median method can be used.
  • the classification unit 24 divides, for example, the cell group that has been processed by the clustering process and the cell group related to the known result information into a plurality of groups.
  • the result presentation unit 25 uses the cell group information (for example, information indicating the progress of the disease) related to the known result information belonging to each of the plurality of groups obtained by the classification unit 24, and the likelihood generated based on the processing data.
  • Information indicating to which group the ratio statistic ⁇ n is classified (that is, to which group the group of cells to be processed is classified) is output via the output unit 14.
  • this result presentation unit 25 may further present a survival curve (Kaplan- Mayer survival curve) of a cell group related to known result information belonging to each group using the result of this classification.
  • a survival curve Kerplan- Mayer survival curve
  • This embodiment has the above configuration and operates as follows.
  • a group of cells collected from a subject as a treatment target is subjected to measurement using the flow cytometry device 2.
  • the cell group classification apparatus 1 obtains measurement data obtained from the cell group to be processed from the flow cytometry device 2
  • the m-dimensional distribution data is processed as processing data based on the measurement data (a plurality of parameters). Generate.
  • likelihood ratio statistics ⁇ n_1, ⁇ n_2, and ⁇ n_3 for each of the two-dimensional distribution data corresponding to each of the first to third two-dimensional distribution data are calculated. Will be calculated.
  • the cell group classification device 1 uses the likelihood ratio statistics ⁇ n_1, ⁇ n_2, ⁇ n_3 for each of the two-dimensional distribution data corresponding to each of the first to third two-dimensional distribution data for each of the r types of cell surface markers. Are further accumulated to obtain r likelihood ratio statistics ⁇ n for each of the r cell surface markers.
  • the cell group classification device 1 stores the r-dimensional vector having the likelihood ratio statistic ⁇ n for each of the r types of cell surface markers generated based on the processing data as an element, and is stored in the storage unit 12.
  • Clustering processing is performed by a widely known method using r-dimensional vectors having likelihood ratio statistics ⁇ n as elements of r types of cell surface markers for each of a plurality of cell groups included in the result information.
  • the cell group classification device 1 determines which cell group information (for example, information indicating the progress of the disease) related to the known result information belonging to each of the plurality of groups and the likelihood ratio statistic ⁇ n generated based on the processing data.
  • Information indicating whether the cells are classified into groups is output via the output unit 14.
  • the measurement data of the cell group sample diagnosed as normal is divided according to the age group of the provider of each cell group sample, and the storage unit 12 is illustrated in FIG.
  • information that represents the age group information that can be specified by the lowest age and the highest age that belong to the age group
  • population data that is generated based on the measurement data for each age group population data for each age group
  • control unit 11 accepts the age of the provider of the cell group to be processed from the operation unit 13 or the like.
  • the population data acquisition unit 22 acquires population data for each age group corresponding to the age group to which the accepted age belongs, and outputs the population data to the testing unit 24.
  • inspection part 24 tests the similarity of the acquired population data for every age group, and the process data obtained from the cell group used as the process target.
  • the obtained 3r two-dimensional data was used.
  • the present embodiment is not limited to this.
  • r three-dimensional data (distribution data) is obtained with the X axis as FSC, the Y axis as SSC, and the Z axis as each cell surface marker. It is good also as examining the similarity with the population data regarding the cell surface marker corresponding to.
  • distribution data including data relating to a plurality of cell surface markers may be used.
  • processing may be performed using m-dimensional distribution data as it is. Specifically, in this example, population data corresponding to m-dimensional distribution data relating to r types of cell surface markers stored in the storage unit 12 is generated and stored in the storage unit 12.
  • the cell group classification device 1 tests the similarity between the m-dimensional distribution data of the generated processing data dp and the m-dimensional distribution data of the population data de read from the storage unit 12. This test is similar to that already described, and the cell group classification apparatus 1 uses the m for the distribution data dp (x1, x2,... Xm), de (x1, x2,... Xm) in a pair of m-dimensional spaces.
  • the known result information is also associated with one likelihood ratio statistic ⁇ n for each cell group based on the m-dimensional distribution data. Then, the cell group classification device 1 performs clustering processing using a known method (for example, the longest distance method) using the known result information and the likelihood ratio statistic ⁇ n generated based on the processing data.
  • a known method for example, the longest distance method
  • the cell group classification device 1 determines which cell group information (for example, information indicating the progress of the disease) related to the known result information belonging to each of the plurality of groups and the likelihood ratio statistic ⁇ n generated based on the processing data.
  • Information indicating whether the cells are classified into groups is output via the output unit 14.
  • the storage unit 12 includes information indicating an age group (information that can be specified by the minimum age and the maximum age belonging to the age group), and measurement for each age group. You may hold
  • control unit 11 receives the age of the provider of the cell group to be processed from the operation unit 13 or the like, and the fixed population data acquisition unit 22 belongs to the age group to which the received age belongs.
  • Population data corresponding to age groups is acquired and output to the test unit 24.
  • inspection part 24 tests the similarity of the acquired population data for every age group, and the process data obtained from the cell group used as the process target.
  • the cell group classification device 1 uses variations in signal intensity distribution of cell surface markers for all collected cells without performing gating that extracts specific cells.
  • the difference from the variation in the signal intensity distribution of the cell surface marker obtained from the group of cells diagnosed as normal is quantified and handled.
  • the prognosis can be predicted based on the correlation between the quantification result and the prognosis.
  • the two-dimensional distribution data generated here is three-dimensional distribution data obtained by plotting data measured by assigning fluorescence intensity (measurement data) of FSC, SSC, and cell surface markers to respective axes of orthogonal three-dimensional coordinates. Are projected onto a plane including the FSC axis and the SSC axis, a plane including the FSC axis and the cell surface marker measurement data axis, and a plane including the SSC axis and the cell surface marker measurement data axis, respectively.
  • This is a density plot showing the number of data at each point on each surface. Specifically, as shown in FIG. 5, second and third two-dimensional distribution data and the like are obtained from the three-dimensional distribution data. Also, the corresponding two-dimensional distribution data for each sample diagnosed as normal is accumulated (added the density value for the same point), and the accumulated first to third two-dimensional distribution data are obtained. Obtained.
  • This adaptive partitioning method is as follows. That is, as illustrated in FIG. 6, the two-dimensional distribution data to be processed is virtually divided into 2 ⁇ 2 congruent regions, and whether the densities in the divided regions are equal to each other (first 1 hypothesis) is tested by chi-square test (S1). Similarly, the two-dimensional distribution data to be processed is virtually divided into 4 ⁇ 4 congruent areas, and whether or not the densities in the divided areas are equal to each other (second hypothesis). Test by chi-square test (S2).
  • the two-dimensional distribution data to be processed is divided into 2 ⁇ 2 congruent regions and the respective two-dimensional distributions are divided.
  • Data is generated (S3), and the processes of steps S1, S2, and S3 are recursively repeated for each of the generated two-dimensional distribution data (S4).
  • the processing result (density distribution function) obtained for the first to third two-dimensional distribution data was used as population data related to CD34.
  • the above processing was similarly performed for the cell surface marker CD41a, and population data related to CD41a was obtained. In the data obtained by this, as shown in FIG. 7, the distribution is smoothed and the data noise is reduced.
  • the fluorescence intensity of CD34 which is FSC (forward scattered light), SSC (side scattered light), and cell surface marker is measured with a flow cytometry instrument. Measurement data was obtained. From this, the first two-dimensional distribution data (density plot) in which the X-axis is FSC and the Y-axis is SSC, and the second two-dimensional distribution data (density) in which the X-axis is FSC and the Y-axis is cell surface marker measurement data Plot) and third two-dimensional distribution data (density plot) in which the X-axis is SSC and the Y-axis is measurement data of the cell surface marker.
  • FSC forward scattered light
  • SSC side scattered light
  • cell surface marker cell surface marker
  • the adaptive partitioning method was processed for each of the first to third two-dimensional distribution data, and the density distribution function was estimated.
  • the processing of the adaptive partitioning method here is the same as the method used when generating the population data, and thus repeated description is omitted.
  • the fluorescence intensity measurement data related to the cell surface marker CD41a is similarly processed, and the density distribution function for the first to third two-dimensional distribution data related to CD41a is obtained. It was.
  • each two-dimensional distribution data dp (x1, x2,..., X6) (data x1 to x3 related to CD34 and data x4 to x6 related to CD41a) based on the cell group obtained from the patient, and population data de (X1, x2,..., X6) (data x1 to x3 related to CD34 and data x4 to x6 related to CD41a) when each two-dimensional space is divided into a plurality of regions (bins) R1, R2.
  • FIG. 8 shows a density distribution function based on the second and third two-dimensional distribution data related to CD34 for the cell group obtained from the patient (the upper left and right data), and a second related to CD34 for the population data.
  • 4 shows an example in which likelihood ratio statistics ⁇ n_2 and ⁇ n_3 obtained from a density distribution function (bottom two left and right data) based on the third two-dimensional distribution data are calculated.
  • ⁇ n_2 0.12
  • ⁇ n_3 0.153.
  • FIG. 9 shows the likelihood ratio statistics ⁇ n_2 and ⁇ n_3 obtained from the density distribution function based on the second and third two-dimensional distribution data related to CD34 and the CD41a for 59 patients.
  • An example in which a distribution with likelihood ratio statistics ⁇ n_5 and ⁇ n_6 obtained from the density distribution function based on the second and third two-dimensional distribution data is generated for each patient is shown.
  • the horizontal axis represents the patient number (1 to 59), and the vertical axis represents the likelihood ratio statistic.
  • group 1 There is a group with a relatively large median distribution (median values of ⁇ n_2, ⁇ n_3, ⁇ n_5, ⁇ n_6), and when the median value exceeds 0.5 and the median value is 0.5 or less based on 0.5 Into two clusters.
  • group 1 a cluster having a median value greater than 0.5 is referred to as group 1, and a cluster having a median value of 0.5 or less is referred to as group 2.
  • FIG. 10 shows a survival curve (Kaplan- Mayer survival curve) representing the overall survival rate for each of the groups 1 and 2 described above.
  • p 0.0408, which was recognized as significant at the 5% level.
  • the median survival was 1.88 years for group 1 and 4.66 years for group 2.
  • the 5-year survival rate was 30% in Group 1 and 43.7% in Group 2.
  • classification is performed using the variation in the signal intensity of the cell surface marker for all the collected cells, and the difference from the variation in the distribution of the signal intensity of the cell surface marker obtained from a group of cells diagnosed as normal It is also understood that the likelihood ratio statistic, which is the quantification result of, correlates with the prognosis. That is, by performing the above processing for other patients, it becomes possible to determine which group has a different prognosis depending on whether or not the median of the likelihood ratio statistics exceeds 0.5. Yes.

Landscapes

  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Biomedical Technology (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Theoretical Computer Science (AREA)
  • Apparatus Associated With Microorganisms And Enzymes (AREA)
  • Investigating Or Analysing Biological Materials (AREA)

Abstract

Ce procédé de classification de groupes cellulaires comprend : la conservation de données de population obtenues sur la base de données mesurées d'échantillons de groupes cellulaires qui ont été diagnostiqués en tant que normales ; la création de données de traitement, dont la similarité aux données de population peut être analysée, sur la base des données mesurées obtenues à propos de groupes cellulaires qui doivent être traités ; l'analyse de la similarité des données de traitement aux données de population conservées par un moyen de conservation ; et la classification des groupes cellulaires qui doivent être traitées en de multiples groupes prédéfinis selon les critères prescrits sur la base des résultats des essais.
PCT/JP2014/050720 2013-01-17 2014-01-16 Appareil pour la classification de groupes cellulaires et procédé de classification de groupes cellulaires WO2014112567A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
JP2013006783 2013-01-17
JP2013-006783 2013-01-17

Publications (1)

Publication Number Publication Date
WO2014112567A1 true WO2014112567A1 (fr) 2014-07-24

Family

ID=51209656

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2014/050720 WO2014112567A1 (fr) 2013-01-17 2014-01-16 Appareil pour la classification de groupes cellulaires et procédé de classification de groupes cellulaires

Country Status (1)

Country Link
WO (1) WO2014112567A1 (fr)

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPS63191044A (ja) * 1987-02-03 1988-08-08 Omron Tateisi Electronics Co 細胞分析装置
WO2005050479A1 (fr) * 2003-11-21 2005-06-02 National University Corporation Kochi University Appareil de recherche de structures similaires, procede de recherche de structures similaires, programme de recherche de structures similaires et appareil de fractionnement

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPS63191044A (ja) * 1987-02-03 1988-08-08 Omron Tateisi Electronics Co 細胞分析装置
WO2005050479A1 (fr) * 2003-11-21 2005-06-02 National University Corporation Kochi University Appareil de recherche de structures similaires, procede de recherche de structures similaires, programme de recherche de structures similaires et appareil de fractionnement

Similar Documents

Publication Publication Date Title
JP5425814B2 (ja) サポートベクタマシンを用いてフローサイトメトリーデータを分析するための方法及びシステム
KR102094326B1 (ko) 위장암의 위험을 평가하는 방법 및 시스템
EP2700042B1 (fr) Analyse de l'expression de biomarqueurs dans des cellules avec des moments
US20180106787A1 (en) System, method, and article for detecting abnormal cells using multi-dimensional analysis
Finak et al. Optimizing transformations for automated, high throughput analysis of flow cytometry data
US20160169786A1 (en) Automated flow cytometry analysis method and system
JP7361395B2 (ja) 表示制御装置、表示制御方法及び表示制御プログラム
US20170102310A1 (en) Flow cytometer and a multi-dimensional data classification method and an apparatus thereof
WO2018151680A1 (fr) Procédés et dispositifs d'identification de populations cellulaires dans des données
CN107389536B (zh) 基于密度-距离中心算法的流式细胞粒子分类计数方法
US10274412B2 (en) Flow cytometry data segmentation result evaluation systems and methods
WO2014112567A1 (fr) Appareil pour la classification de groupes cellulaires et procédé de classification de groupes cellulaires
US10482314B2 (en) Automatic calculation for ploidy classification
KR102225231B1 (ko) 엑소좀 miRNA를 기준으로 암 환자를 판별하는 방법 및 장치
NL1040872C2 (en) Method of analysing data from, computer program, computer readable medium and data analysis system.
WO2024185887A1 (fr) Système, procédé et programme de spécification de population de cellules
CN108169105B (zh) 应用于血细胞分析仪的白细胞分类处理方法
Bashashati et al. A pipeline for automated analysis of flow cytometry data: preliminary results on lymphoma sub-type diagnosis
Caligola et al. GateMeClass: Gate Mining and Classification of cytometry data
Thairu Quality control and analysis of ow cytometry data
US20160125134A1 (en) Automated Classification of Cells in Biologic Mixtures Analyzed by High Parameter Cytometry Instrumentation, Processing, System and Method
Templ et al. Statistical Indicators for the Analysis of Digitalized Brain Tumor Images

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 14740665

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 14740665

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: JP