Computer technology is disclosed that applies innovative data processing and visualization techniques to large multi-parameter data sets such as cellular gene expression data to find new relationships such as relationships between cells and genes and create new associative data structures within the data sets that represent these relationships. For example, scatterplots of gene expression data can be iteratively pivoted between a cell view and a gene view to find cell populations and gene sets of interest to a user.
Methods and systems for a deep-learning platform for sorting cell populations. An example method includes executing a software-platform associated with analyzing received flow cytometry data obtained via an acquisition device in communication with the computing system, and the software-platform sorting cell populations indicated in the flow cytometry data. User input is received indicating selection of a deep-learning module, the deep-learning module being obtained via a network to supplement the software-platform. The flow cytometry data is analyzed and a machine learning model is selected which was trained based on similar phenotype information as indicated in the flow cytometry data. The machine learning model is applied based on the flow cytometry data, the information being normalized based on the UMI counts associated with the flow cytometry data. A graphical representation of cell populations indicated in the flow cytometry data is presented, the graphical representation sorting the cell populations according to phenotype information.
Methods and systems for a deep-learning platform for sorting cell populations. An example method includes executing a software-platform associated with analyzing received flow cytometry data obtained via an acquisition device in communication with the computing system, and the software-platform sorting cell populations indicated in the flow cytometry data. User input is received indicating selection of a deep-learning module, the deep-learning module being obtained via a network to supplement the software-platform. The flow cytometry data is analyzed and a machine learning model is selected which was trained based on similar phenotype information as indicated in the flow cytometry data. The machine learning model is applied based on the flow cytometry data, the information being normalized based on the UMI counts associated with the flow cytometry data. A graphical representation of cell populations indicated in the flow cytometry data is presented, the graphical representation sorting the cell populations according to phenotype information.
G01N 15/14 - Optical investigation techniques, e.g. flow cytometry
G16B 20/40 - Population geneticsLinkage disequilibrium
G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
G16B 50/30 - Data warehousingComputing architectures
Scientific instruments can be network-enabled by adding a wireless communication capability to the computers associated with those scientific instruments. Through this wireless communication capability, the scientific data acquired by a scientific instrument and metadata about that scientific data can be wirelessly transferred from the instrument-associated computer to a data hub. By way of example, a wireless personal area network (PAN) can be established between the instrument-associated computer and the data hub. From the data hub, the scientific data can be further communicated to remote servers via another network connection. Furthermore, in another example embodiment, the wireless communication capability between the instrument-associated computer and the data hub can be leveraged as a conduit for passing commands from the data hub or other devices in communication with the data hub to the instrument-associated computer for controlling the operation of the scientific instrument.
G06F 15/16 - Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
H04W 4/80 - Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication
H04W 4/38 - Services specially adapted for particular environments, situations or purposes for collecting sensor information
6.
Visualization, comparative analysis, and automated difference detection for large multi-parameter data sets
Some embodiments of the methods provided herein relate to sample analysis and particle characterization methods for large, multi-parameter data sets. Frequency difference gating compares at least two different data sets to identify regions in a multivariate space where a frequency of events from a first data set is different than a frequency of events from the second data set according to a defined threshold.
G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
G16B 45/00 - ICT specially adapted for bioinformatics-related data visualisation, e.g. displaying of maps or networks
G06F 17/18 - Complex mathematical operations for evaluating statistical data
G01N 15/14 - Optical investigation techniques, e.g. flow cytometry
G06K 9/62 - Methods or arrangements for recognition using electronic means
G06V 10/75 - Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video featuresCoarse-fine approaches, e.g. multi-scale approachesImage or video pattern matchingProximity measures in feature spaces using context analysisSelection of dictionaries
G06V 20/69 - Microscopic objects, e.g. biological cells or cellular parts
7.
VISUALIZATION, COMPARATIVE ANALYSIS, AND AUTOMATED DIFFERENCE DETECTION FOR LARGE MULTI-PARAMETER DATA SETS
Some embodiments of the methods provided herein relate to sample analysis and particle characterization methods for large, multi-parameter data sets. Frequency difference gating compares at least two different data sets to identify regions in a multivariate space where a frequency of events from a first data set is different than a frequency of events from the second data set according to a defined threshold.
Computer technology is disclosed that applies innovative data processing and visualization techniques to large multi-parameter data sets such as cellular gene expression data to find new relationships such as relationships between cells and genes and create new associative data structures within the data sets that represent these relationships. For example, scatterplots of gene expression data can be iteratively pivoted between a cell view and a gene view to find cell populations and gene sets of interest to a user.
Computer technology is disclosed that applies innovative data processing and visualization techniques to large multi-parameter data sets such as cellular gene expression data to find new relationships such as relationships between cells and genes and create new associative data structures within the data sets that represent these relationships. For example, scatterplots of gene expression data can be iteratively pivoted between a cell view and a gene view to find cell populations and gene sets of interest to a user.
Scientific instruments can be network-enabled by adding a wireless communication capability to the computers associated with those scientific instruments. Through this wireless communication capability, the scientific data acquired by a scientific instrument and metadata about that scientific data can be wirelessly transferred from the instrument-associated computer to a data hub. By way of example, a wireless personal area network (PAN) can be established between the instrument-associated computer and the data hub. From the data hub, the scientific data can be further communicated to remote servers via another network connection. Furthermore, in another example embodiment, the wireless communication capability between the instrument-associated computer and the data hub can be leveraged as a conduit for passing commands from the data hub or other devices in communication with the data hub to the instrument-associated computer for controlling the operation of the scientific instrument.
Scientific instruments can be network-enabled by adding a wireless communication capability to the computers associated with those scientific instruments. Through this wireless communication capability, the scientific data acquired by a scientific instrument and metadata about that scientific data can be wirelessly transferred from the instrument-associated computer to a data hub. By way of example, a wireless personal area network (PAN) can be established between the instrument-associated computer and the data hub. From the data hub, the scientific data can be further communicated to remote servers via another network connection. Furthermore, in another example embodiment, the wireless communication capability between the instrument-associated computer and the data hub can be leveraged as a conduit for passing commands from the data hub or other devices in communication with the data hub to the instrument-associated computer for controlling the operation of the scientific instrument.
G06F 15/16 - Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
H04W 4/80 - Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication
Scientific instruments can be network-enabled by adding a wireless communication capability to the computers associated with those scientific instruments. Through this wireless communication capability, the scientific data acquired by a scientific instrument and metadata about that scientific data can be wirelessly transferred from the instrument-associated computer to a data hub. By way of example, a wireless personal area network (PAN) can be established between the instrument-associated computer and the data hub. From the data hub, the scientific data can be further communicated to remote servers via another network connection. Furthermore, in another example embodiment, the wireless communication capability between the instrument-associated computer and the data hub can be leveraged as a conduit for passing commands from the data hub or other devices in communication with the data hub to the instrument-associated computer for controlling the operation of the scientific instrument.
09 - Scientific and electric apparatus and instruments
41 - Education, entertainment, sporting and cultural services
42 - Scientific, technological and industrial services, research and design
Goods & Services
Computer software for analyzing single cell gene sequencing and gene expression data Educational services, namely, conducting in-person and online classes, seminars, conferences, workshops in the fields of single cell gene sequencing and gene expression [ Cloud computing featuring software for use in analyzing single cell gene sequencing and gene expression data ]
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.
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.
G06F 17/30 - Information retrieval; Database structures therefor
G01N 15/14 - Optical investigation techniques, e.g. flow cytometry
G01N 35/00 - Automatic analysis not limited to methods or materials provided for in any single one of groups Handling materials therefor
G06N 5/00 - Computing arrangements using knowledge-based models
G06F 19/00 - Digital computing or data processing equipment or methods, specially adapted for specific applications (specially adapted for specific functions G06F 17/00;data processing systems or methods specially adapted for administrative, commercial, financial, managerial, supervisory or forecasting purposes G06Q;healthcare informatics G16H)
G06F 19/10 - Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology (in silico methods of screening virtual chemical libraries C40B 30/02;in silico or mathematical methods of creating virtual chemical libraries C40B 50/02)
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.
A framework and interface for invoking and assimilating external algorithms and interacting with the algorithms in-session and real-time are described. Embodiments include 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. Embodiments include an entire discovery or diagnosis process 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.
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.
Disclosed herein are a number of example embodiments for data management and analysis in connection with life science operations such as flow cytometry. For example, disclosed herein are (1) a networked link between an acquisition computer and a computer performing analysis on the acquired data, (2) modular experiment templates that can be divided into individual components for future use in multiple experiments, and (3) an automated pipeline of experiment elements.
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
H04L 29/06 - Communication control; Communication processing characterised by a protocol
G16H 10/40 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
Disclosed herein are a number of example embodiments for data management and analysis in connection with life science operations such as flow cytometry. For example, disclosed herein are (1) a networked link between an acquisition computer and a computer performing analysis on the acquired data, (2) modular experiment templates that can be divided into individual components for future use in multiple experiments, and (3) an automated pipeline of experiment elements.
