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CN-122003591-A - Classification of events in flow cytometry data

CN122003591ACN 122003591 ACN122003591 ACN 122003591ACN-122003591-A

Abstract

The present disclosure relates to a method for analyzing particles within a flow cytometry system using a computing device. The method includes receiving flow cytometry data from a flow cytometry system, assigning a probability value to at least one event, the probability value indicating a probability that the at least one event is within a defined threshold rule to categorize the event as belonging to a cluster, displaying the probability value to a user, allowing the user to adjust the probability threshold, and generating an output. An output is generated by comparing the probability value to a probability threshold, identifying at least one inclusion event when the probability value satisfies the probability threshold, and displaying the at least one inclusion event to the user.

Inventors

  • Ernesto Starosvietsky
  • Gilliana Milagros Mustiga
  • John Steven. Riley

Assignees

  • 贝克曼库尔特有限公司

Dates

Publication Date
20260508
Application Date
20241025
Priority Date
20231030

Claims (15)

  1. 1. A method for analyzing particles within a flow cytometry system using a computing device, the method comprising: receiving flow cytometry data from the flow cytometry system, the flow cytometry data comprising at least one event; Assigning a probability value to the at least one event, the probability value indicating a probability that the at least one event is associated with a population of interest; displaying a probability threshold to a user, wherein the probability threshold classifies an event as belonging to the population of interest; Allowing the user to adjust the probability threshold, and The output is generated by: Comparing the probability value of the at least one event with the probability threshold value, and Classification information is assigned to at least one inclusive event, wherein the at least one inclusive event includes a probability value exceeding the probability threshold.
  2. 2. The method of claim 1, further comprising identifying at least one non-inclusive event as not being within the population of interest when the probability value does not exceed the probability threshold.
  3. 3. The method of claim 2, further comprising filtering the at least one non-inclusive event from the at least one event.
  4. 4. The method of claim 2, wherein the at least one inclusive event and the at least one non-inclusive event are classified using a binary representation.
  5. 5. The method of claim 1, wherein the probability threshold comprises a value between zero and one.
  6. 6. The method of claim 1, wherein the at least one inclusion event is identified by applying one or more filters.
  7. 7. The method of claim 6, wherein the at least one inclusion event is identified by a series of filters including at least one of a bandpass filter, a lowpass filter, and a highpass filter.
  8. 8. The method of claim 6, wherein the at least one inclusion event is categorized into a population of blood cells by boolean logic binary characterization of the event, wherein the at least one event is characterized as an inclusion event or a non-inclusion event.
  9. 9. The method of claim 1, wherein the output is generated using a machine learning algorithm.
  10. 10. The method of claim 9, further comprising generating a confidence rating by comparing the output to an independent dataset, training the machine learning algorithm by a supervised method.
  11. 11. The method of claim 10, further comprising: adjusting an output generated by the machine learning algorithm by changing the probability threshold, and At least one adjusted output is generated.
  12. 12. The method of claim 10, wherein the independent data set is manually adjusted by the user.
  13. 13. The method of claim 10, further comprising locking the machine learning algorithm for use when the confidence rating exceeds a threshold.
  14. 14. The method of claim 1, further comprising: receiving one or more probability value events, and One or more additional outputs corresponding to the one or more additional probability thresholds are generated.
  15. 15. The method of claim 1, further comprising separating a desired number of events for further analysis by: Generating a histogram comprising the at least one event, wherein the histogram corresponds to the number of events having the probability value; Identifying the desired number of events to analyze, and The probability threshold is adjusted to include the desired number of events while excluding other events.

Description

Classification of events in flow cytometry data Cross Reference to Related Applications The present application was filed on 25 th 10 of 2024 as PCT international application, and claims the benefit and priority of U.S. application No. 63/594,165, entitled CLASSIFICATION OF EVENTS IN FLOW CYTOMETRY DATA, filed on 30 th 10 of 2023, the disclosure of which is incorporated herein by reference in its entirety. Background Flow cytometry is a technique for detecting and analyzing chemical and physical characteristics of cells or particles in a fluid sample. For example, flow cytometry may be used to evaluate cells from blood, bone marrow, tumors, or other bodily fluids. Typically, the sample is passed through a fluid nozzle that aligns the particles within the sheath fluid into a single column line (SINGLE FILE LINE). As the particles pass in a single column, the laser beam irradiates the particles to generate radiated light including forward scattered light, side scattered light, and fluorescence. The radiated light may then be detected and analyzed to determine one or more characteristics of the particles. Disclosure of Invention In general, the present disclosure relates to analyzing particles using flow cytometry. In one possible configuration, an AI algorithm is used to automatically classify events using one or more control variables/thresholds that are automatically adjusted. In another possible configuration, one or more control variables/thresholds are manually adjusted to correct classification of events and, if necessary, further augment the training dataset. Various aspects are described in the present disclosure, including but not limited to the following aspects. One aspect relates to a method of analyzing particles within a flow cytometry system using a computing device. The method includes receiving flow cytometry data from a flow cytometry system, the flow cytometry data including at least one event, assigning a probability value to the at least one event, the probability value indicating a probability that the at least one event belongs to a cluster/category of events, displaying the probability value to a user, wherein a probability threshold classifies the event as belonging to a population of interest, allowing the user to adjust the probability threshold, and generating an output. An output is generated by comparing the probability value of the at least one event with a probability threshold and assigning classification information to the at least one inclusive event, wherein the at least one inclusive event comprises a probability value exceeding the probability threshold. Another aspect relates to a method for modulating outcome classification within a flow cytometry system. The method includes examining flow cytometry data, the flow cytometry data including at least one event including a probability value indicative of a probability that the at least one event is associated with a population of interest, allowing a probability threshold on a flow cytometry system to be adjusted to filter out one or more events of the at least one event when the at least one probability value is greater than a probability threshold input, and receiving an output from the flow cytometry system. The output includes at least one inclusion event that exceeds the probability threshold input. Yet another aspect relates to a flow cytometry system for analyzing particles. The flow cytometry system includes at least one processing device and a non-transitory computer readable storage medium storing instructions that, when executed by the processing device, cause the at least one processing device to receive flow cytometry data from the flow cytometry system, the flow cytometry data including at least one event, assign a probability value to the at least one event that indicates a probability that the at least one event is within a population of interest, display the at least one probability value to a user, allow adjustment of a probability threshold from the user, and generate an output. The output is generated by comparing the at least one probability value to a probability threshold, identifying at least one inclusion event when the at least one probability value exceeds the probability threshold, and displaying the at least one inclusion event to a user. Various additional aspects will be set forth in the description that follows. These aspects may relate to individual features and combinations of features. It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the broad inventive concepts upon which the embodiments disclosed herein are based. Drawings The following figures illustrate examples of the present disclosure, and thus do not limit the scope of the present disclosure. Examples of the present disclosure will hereinafter be described in conjunction with the appended drawings, wherein like num