Characterization and sorting for particle analyzers

    公开(公告)号:US11327003B2

    公开(公告)日:2022-05-10

    申请号:US16557539

    申请日:2019-08-30

    Abstract: Non-parametric transforms such as t-distributed stochastic neighbor embedding (tSNE) are used to analyze multi-parametric data such as data derived from flow cytometry or other particle analysis systems and methods. These transforms may be included for dimensionality reduction and identification of subpopulations (e.g., gating). By nature, non-parametric transforms cannot transform new observations without training a new transformation based on the entire dataset including the new observations. The features described parameterize non-parametric transforms using a neural network thereby allowing a small training dataset to be transformed using non-parametric techniques. The training dataset may then be used to generate an accurate parametric model for assessing additional events in a manner consistent with the initial events.

    Characterization and Sorting for Particle Analyzers

    公开(公告)号:US20210255087A1

    公开(公告)日:2021-08-19

    申请号:US17225567

    申请日:2021-04-08

    Abstract: Some embodiments of the methods provided herein relate to sample analysis and particle characterization methods. Some such embodiments include receiving, from a particle analyzer, measurements for a first portion of particles associated with an experiment. Some embodiments also include generating a tree representing groups of related particles based at least in part on the measurements, wherein the tree includes at least three groups. Some embodiments also include generating a measure of relatedness between a first group and a second group of the tree based at least in part on the measurements. Some embodiments also include and configuring the particle analyzer to classify a subsequent particle associated with the experiment with the first group real-time, wherein the subsequent particle is not included in the first portion of particles. Some embodiments also include sorting the subsequent particle.

    Sample behavior analyzer control
    3.
    发明授权

    公开(公告)号:US10739248B2

    公开(公告)日:2020-08-11

    申请号:US16179646

    申请日:2018-11-02

    Abstract: Methods and systems are disclosed for controlling a particle analyzer based at least in part on a generated entrainment factor for a sample. The features include flowing a sample with a series of particles through the particle analyzer, detecting events and calculating an expected frequency of those events based on a distribution, such as a Poisson distribution, and measuring an observed frequency of particle events. An entrainment factor may be generated from a ratio of observed event frequency to expected event frequency. Further adjustment to the particle analyzer maybe performed based on the indicated entrainment factor such as adjusted sorting bias.

    Methods and systems for assessing sample behavior in a flow cytometer

    公开(公告)号:US10190963B2

    公开(公告)日:2019-01-29

    申请号:US14170409

    申请日:2014-01-31

    Abstract: Methods and systems are disclosed for generating an entrainment factor in a flow cytometry sample. The methods comprise flowing a sample with a series of particles through the flow cytometer, detecting events and calculating an expected frequency of those events based on a distribution, such as a Poisson distribution, and measuring an observed frequency of particle events. An entrainment factor may be generated from a ratio of observed event frequency to expected event frequency. Further adjustment to the flow cytometer maybe performed based on the indicated entrainment factor such as adjusted sorting bias.

    Characterization and sorting for particle analyzers

    公开(公告)号:US11686663B2

    公开(公告)日:2023-06-27

    申请号:US17225567

    申请日:2021-04-08

    Abstract: Some embodiments of the methods provided herein relate to sample analysis and particle characterization methods. Some such embodiments include receiving, from a particle analyzer, measurements for a first portion of particles associated with an experiment. Some embodiments also include generating a tree representing groups of related particles based at least in part on the measurements, wherein the tree includes at least three groups. Some embodiments also include generating a measure of relatedness between a first group and a second group of the tree based at least in part on the measurements. Some embodiments also include and configuring the particle analyzer to classify a subsequent particle associated with the experiment with the first group real-time, wherein the subsequent particle is not included in the first portion of particles. Some embodiments also include sorting the subsequent particle.

    Characterization and sorting for particle analyzers

    公开(公告)号:US11002658B2

    公开(公告)日:2021-05-11

    申请号:US16390754

    申请日:2019-04-22

    Abstract: Some embodiments of the methods provided herein relate to sample analysis and particle characterization methods. Some such embodiments include receiving, from a particle analyzer, measurements for a first portion of particles associated with an experiment. Some embodiments also include generating a tree representing groups of related particles based at least in part on the measurements, wherein the tree includes at least three groups. Some embodiments also include generating a measure of relatedness between a first group and a second group of the tree based at least in part on the measurements. Some embodiments also include and configuring the particle analyzer to classify a subsequent particle associated with the experiment with the first group real-time, wherein the subsequent particle is not included in the first portion of particles. Some embodiments also include sorting the subsequent particle.

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