Becton Dickinson and Company Limited

University of Colorado

Lin, Jonathan

Trotter, Joseph

Owsley, Keegan

Joseph T Trotter

Keegan Owsley

Jonathan Lin

Nikolay Samusik

Brian David Warner

Liping Yu

Allison Irvine

Ming Yan

Paul Barclay Purcell

Christopher B Wolf

Robert A Hoffman

Sujata Iyer

Alan M Stall

Theodore W Randolph

John Frank Carpenter

Christopher J Wolf

Jean-Bernard Hamel

John Paul Gabrielson

Aspects of the present disclosure include methods for processing and scaling cytometric data. Methods according to certain embodiments include obtaining cytometric data for a sample, wherein the cytometric data comprises measurements of a plurality of parameters from particles irradiated in the sample flowing in a flow stream; identifying a parameter of interest; specifying positive and negative measurement intervals on the parameter of interest; scaling the cytometric data by transforming the parameter of interest based at least in part on the corresponding specified positive and negative intervals. Systems for practicing the subject methods are also provided. Non-transitory computer readable storage mediums are also described.

Method for optimal scaling of cytometry data for machine learning analysis and systems for same

A cell sorting system that automatically generates a sorting strategy based on examples of target events provided by an operator. The target events can be selected using measurements ranging from traditional flow cytometry measurements to derived measurements that are computationally expensive to complex measurements such as images.

Adaptive sorting for particle analyzers

Disclosed herein include systems, devices, and methods for determining a gating strategy. An acquisition system can cause a particle analyzer to collect parameter measurements of some particles of a sample. An analysis system can receive the parameter measurements and determine a gating strategy from the parameter measurements. The acquisition can collect measurements of some or all of the remaining particles of the sample using the gating strategy determined by the analysis system.

Optimized sorting gates

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

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.

Methods and systems for efficient contour and gating in flow cytometry are provided. Event data is compressed to reduce the number of points needed to represent polygon contours for the event data. Selection of a level within the contour then causes the generation of a gate. This allows limited resource devices, such as touchscreen wireless devices, to render and gate flow cytometry data in a resource efficient manner.

Efficient contours and gating

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.

Sample behavior analyzer control

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.

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La Jolla, CA, United States of America

Joseph T Trotter