Data discovery nodes

• US 10,713,572 B2
• Filed: 05/09/2016
• Issued: 07/14/2020
• Est. Priority Date: 05/08/2015
• Status: Active Grant

• Current Assignee: FlowJo, LLC (Becton, Dickinson & Co)
• Original Assignee: FlowJo, LLC (Becton, Dickinson & Co)
• Inventors: Simm, Maciej, Almarode, Jay, Stadnisky, Michael D.
• Primary Examiner(s): Afshar, Kamran
• Assistant Examiner(s): Buss, Benjamin J
• Application Number:
  US15/150,095
• Publication Number:
  US 20160328649A1
• Time in Patent Office: 1,527 Days
• CPC Class Codes: G06F 16/164 File meta data generation
• CPC Class Codes: G06F 9/44526 Plug-ins; Add-ons
• CPC Class Codes: G06N 5/01 Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition
• CPC Class Codes: G16B 50/00 ICT programming tools or database systems specially adapted for bioinformatics
• CPC Class Codes: G16B 50/30 Data warehousing; Computing architectures
• CPC Class Codes: H04L 63/102 Entity profiles
• CPC Class Codes: H04L 67/10 in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46
• CPC Class Codes: H04L 67/141 Setup of application sessions admission control or resource allocation in data switching networks H04L47/70
• CPC Class Codes: H04L 67/143 Termination or inactivation of sessions, e.g. event-controlled end of session
• CPC Class Codes: H04L 67/53 using third party service providers

Abstract

A framework and interface for invoking and assimilating external algorithms and interacting with said algorithms in-session and real-time are described herein. An example embodiment also includes reproducible, updatable nodes that can be leveraged for data-driven analysis whereby the data itself can direct the algorithm choice, variables, and presentation leading to iteration and optimization in an analysis workflow. With example embodiments, an entire discovery or diagnosis process may be executed on a particular data set, thereby divorcing the discovery or diagnosis process from a specific data set such that the same discovery or diagnosis process, phenotype identification, and visualizations may be repeated on future experiments, published, validated, or shared with another investigator.

Claims

1. A computer program product for processing scientific data according to a model that is independent of any specific data set, the computer program product comprising:

a data discovery node data structure resident on a non-transitory computer-readable storage medium, the data discovery node data structure comprising (1) a specification of scientific data to be subjected to an iterative scientific data analysis, (2) a specification of an output format for the iterative scientific data analysis, and (3) a specification of a plurality of operational variables for controlling the iterative scientific data analysis, the specified operational variables comprising (i) a specification of an algorithm to be performed on the specified scientific data as part of the iterative scientific data analysis, (ii) a specification of metadata, the specified metadata configured to define conditions under which the specified algorithm will be applied to the specified scientific data, and (iii) a specification of a satisfaction variable, the specified satisfaction variable configured to control how many iterations are performed as part of the iterative scientific data analysis; and

a plurality of processor-executable instructions that are resident on a non-transitory computer-readable storage medium, wherein the instructions are configured, upon execution by a processor of a computer, to cause the computer to;

read and invoke the data discovery data structure to perform the iterative scientific data analysis on a specific data set corresponding to the specified scientific data according to the specified operational variables, determine whether the metadata meets a metadata rule criteria specified by one of the plurality of operational variables, and generate a result in the specified output format,

wherein the step of determining whether the metadata meets a metadata rule criteria comprises testing the metadata against the metadata rule criteria according to a mode selected from the group consisting of a loose mode, a moderate mode, and a strict mode,

wherein the loose mode specifies the metadata has no requirements to meet the metadata rule criteria,

wherein the moderate mode specifies that the metadata must meet a number of criteria of the metadata rule criteria over a user-set threshold, and

wherein the strict mode specifies that the metadata must meet all criteria of the metadata rule criteria.

2. The computer program product of claim 1 wherein the instructions are further configured, upon execution by the processor, to cause the computer, as part of the read and invocation operations, to:

load the specified operational variables into memory;

test the loaded metadata against the specific data set;

determine a specified algorithm to be performed on the specific data set based on the metadata test operation;

apply the determined algorithm to the specified data set to produce a data analysis result;

create a temporary data object in memory that stores the result and a state for the iterative scientific data analysis;

determine whether another iteration of the iterative scientific data analysis is needed based on the loaded satisfaction variable;

in response to a determination that another iteration of the iterative scientific data analysis is needed, (1) repeat the metadata test operation, the specified algorithm determination, the algorithm application operation, and the another iteration determination operation until a determination is made that the loaded satisfaction variable has been satisfied, and (2) update the temporary data object based on the repeated algorithm application operation; and

in response to a determination that another iteration of the scientific data analysis is not needed, write a result of the iterative scientific data analysis into a workspace in accordance with the specified output format.

3. The computer program product of claim 1 further comprising a plurality of the data discovery node data structures resident on the non-transitory computer-readable storage medium, wherein a plurality of the data discovery node data structures comprise different specifications relative to each other, and wherein the instructions are further configured, upon execution by the processor, to cause the computer to:

select a data discovery node data structure from among the plurality of data discovery node data structures in response to user input; and

perform the read and invocation operations on the selected data discovery node data structure.

4. The computer program product of claim 3 wherein the instructions are further configured, upon execution by the processor, to cause the computer to:

define at least one of the specified operational variables for the selected data discovery node data structure in response to user input.

5. The computer program product of claim 1 wherein the specified algorithm comprises an external algorithm, and wherein the instructions are further configured, upon execution by the processor, to cause the computer to:

invoke the external algorithm via an external algorithm plug-in interface framework.

6. The computer program product of claim 1 wherein the specified algorithm is executed by a remote computer, and wherein the instructions are further configured, upon execution by the processor, to cause the computer to:

invoke the specified algorithm via a remote computer plug-in interface framework.

7. The computer program product of claim 1 wherein the scientific data comprises cell data from an experiment.

8. The computer program product of claim 7 wherein the cell data comprises single cell data acquired by a flow cytometer.

9. The computer program product of claim 7 wherein the specified algorithm comprises a population identification algorithm.

10. The computer program product of claim 7 wherein the specified algorithm comprises a geometric gating algorithm.

11. A method for analyzing scientific data comprising:

applying a data discovery node data structure to a data file, the data file comprising scientific data collected by an acquisition instrument, the data file having metadata associated therewith, wherein the applying step comprises;

loading a plurality of operational variables associated with the data discovery node and the metadata associated with the data file into memory;

determining whether the metadata meets a metadata rule criteria specified by one of the plurality of operational variables; and

in response to a determination that the metadata meets the metadata rule criteria;

loading the scientific data associated with the data file into memory;

executing a first analysis algorithm on the scientific data associated with the data file, wherein one of the plurality of operational variables specifies the first analysis algorithm;

creating a temporary data object that defines a satisfaction variable;

determining whether the temporary data object'"'"'s satisfaction variable satisfies a satisfaction threshold specified by one of the plurality of operational variables; and

in response to a determination that the temporary data object'"'"'s satisfaction variable does not satisfy the satisfaction threshold, (1) executing either the first analysis algorithm or a second analysis algorithm on a full set or a subset of the scientific data associated with the data file, wherein one of the plurality of operational variables defines whether to apply the first analysis algorithm or the second analysis algorithm to the full set or the subset of the raw data, and (2) updating the temporary data object based on the executing of the first analysis algorithm or the second analysis algorithm; and

repeatedly performing the steps of (1) determining whether the temporary data object'"'"'s satisfaction variable satisfies the satisfaction threshold, (2) executing either the first analysis algorithm or the second analysis algorithm, and (3) updating the temporary data object until the updated temporary data object'"'"'s satisfaction variable satisfies the satisfaction threshold,

wherein the step of determining whether the metadata meets a metadata rule criteria comprises testing the metadata against the metadata rule criteria according to a mode selected from the group consisting of a loose mode, a moderate mode, and a strict mode,

wherein the loose mode specifies the metadata has no requirements to meet the metadata rule criteria,

wherein the moderate mode specifies that the metadata must meet a number of criteria of the metadata rule criteria over a user-set threshold,

wherein the strict mode specifies that the metadata must meet all criteria of the metadata rule criteria, and

wherein the method steps are performed by a processor.

12. The method of claim 11 wherein the data discovery node is a pre-defined data structure saved in a database.

13. The method of claim 11 wherein the data discovery node is a newly defined data structure created by a user.

14. The method of claim 13 further comprising storing the newly defined data discovery node in a database for future use.

15. The method of claim 14 further comprising:

the processor searching for a similar data discovery node in the database; and

analyzing parameters of the similar data discovery node and comparing the parameters of the similar data discovery node with the parameters of the data discovery node.

16. The method of claim 11 wherein the operational variables comprise a plurality of acquisition instrument parameters, feature variables, iteration variables, and range variables.

17. A computer program product comprising:

a plurality of processor-executable instructions that are resident on a non-transitory computer-readable storage medium, wherein the instructions are configured for execution by the processor to analyze scientific data by causing the computer to;

apply a node data structure to a data file, the data file comprising scientific data collected by an acquisition instrument, the data file having metadata associated therewith, wherein the apply operation is configured to;

load a plurality of operational variables associated with the data discovery node and the metadata associated with the data file into memory;

determine whether the metadata meets a metadata rule criteria specified by one of the plurality of operational variables; and

in response to a determination that the metadata meets the metadata rule criteria;

load the scientific data associated with the data file into memory;

execute a first analysis algorithm on the scientific data associated with the data file, wherein one of the plurality of operational variables specifies the first analysis algorithm;

create a temporary data object that defines a satisfaction variable;

determine whether the temporary data object'"'"'s satisfaction variable satisfies a satisfaction threshold specified by one of the plurality of operational variables; and

in response to a determination that the temporary data object'"'"'s satisfaction variable does not satisfy the satisfaction threshold, (1) execute either the first analysis algorithm or a second analysis algorithm on a full set or a subset of the scientific data associated with the data file, wherein one of the plurality of operational variables defines whether to apply the first analysis algorithm or the second analysis algorithm to the full set or the subset of the raw data, and (2) update the temporary data object based on the executing of the first analysis algorithm or the second analysis algorithm; and

repeatedly perform the (1) determination operation as whether the temporary data object'"'"'s satisfaction variable satisfies the satisfaction threshold, (2) the first analysis algorithm or the second analysis algorithm execution operation, and (3) the update operation until the updated temporary data object'"'"'s satisfaction variable satisfies the satisfaction threshold,

wherein the step of determining whether the metadata meets a metadata rule criteria comprises testing the metadata against the metadata rule criteria according to a mode selected from the group consisting of a loose mode, a moderate mode, and a strict mode,

wherein the loose mode specifies the metadata has no requirements to meet the metadata rule criteria,

wherein the moderate mode specifies that the metadata must meet a number of criteria of the metadata rule criteria over a user-set threshold, and

wherein the strict mode specifies that the metadata must meet all criteria of the metadata rule criteria.

18. A method for analyzing scientific data comprising:

receiving a specification of a plurality of operational variables, wherein the specification comprises (1) a specification of a satisfaction criteria, (2) a specification of a first analysis algorithm, (3) a specification of a second analysis algorithm, and (4) a specification of metadata specifying conditions under which the first and second analysis algorithms are to be applied to the scientific data;

executing the first analysis algorithm on at least a portion of the scientific data based on the operational variable that specifies the first analysis algorithm and the operational variable that specifies the conditions under which the first analysis algorithm is to be applied to the scientific data; and

repeatedly executing the first analysis algorithm or a second analysis algorithm on at least a portion of the scientific data based on the results of the executing step and the operational variables until the satisfaction criteria is met; and

determining whether the metadata meets a metadata rule criteria specified by one of the plurality of operational variables,

wherein the step of determining whether the metadata meets a metadata rule criteria comprises testing the metadata against the metadata rule criteria according to a mode selected from the group consisting of a loose mode, a moderate mode, and a strict mode,

wherein the loose mode specifies the metadata has no requirements to meet the metadata rule criteria,

wherein the moderate mode specifies that the metadata must meet a number of criteria of the metadata rule criteria over a user-set threshold,

wherein the strict mode specifies that the metadata must meet all criteria of the metadata rule criteria, and

wherein the method steps are performed by a processor.

19. The method of any of claim 18 wherein one of the plurality of operational variables specifies whether to analyze the scientific data or the subset of scientific data.

20. The method of claim 18 further comprising the processor creating a temporary object comprising a satisfaction value, wherein the processor updates the satisfaction value after the processor executes an analysis algorithm on data;

and

the processor comparing the value of the satisfaction value to the satisfaction criteria to determine whether the satisfaction criteria is met.

21. The method of claim 18 wherein the processor determines whether to execute the first or the second analysis algorithm based on results generated by the processor executing the first analysis algorithm on the scientific data.

22. The method of claim 18 wherein the processor determines whether to analyze the scientific data or the subset of scientific data based on results generated by the processor executing the first analysis algorithm on the scientific data.

23. The method of claim 18 wherein at least one of the specified analysis algorithms comprises an external analysis algorithm.

24. A computer program product comprising:

a plurality of processor-executable instructions that are resident on a non-transitory computer-readable storage medium, wherein the instructions are configured for execution by the processor to analyze scientific data by causing the computer to;

receive a specification of a plurality of operational variables, wherein the specification comprises (1) a specification of a satisfaction criteria, (2) a specification of a first analysis algorithm, (3) a specification of a second analysis algorithm, and (4) a specification of metadata specifying conditions under which the first and second analysis algorithms are to be applied to the scientific data;

execute the first analysis algorithm on at least a portion of the scientific data based on the operational variable that specifies the first analysis algorithm and the operational variable that specifies the conditions under which the first analysis algorithm is to be applied to the scientific data; and

repeatedly execute the first analysis algorithm or a second analysis algorithm on at least a portion of the scientific data based on the results of the executing step and the operational variables until the satisfaction criteria is met;

determining whether the metadata meets a metadata rule criteria specified by one of the plurality of operational variables,

wherein the step of determining whether the metadata meets a metadata rule criteria comprises testing the metadata against the metadata rule criteria according to a mode selected from the group consisting of a loose mode, a moderate mode, and a strict mode,

wherein the loose mode specifies the metadata has no requirements to meet the metadata rule criteria,

wherein the moderate mode specifies that the metadata must meet a number of criteria of the metadata rule criteria over a user-set threshold, and

wherein the strict mode specifies that the metadata must meet all criteria of the metadata rule criteria.