Abstract: 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.

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.

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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

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.

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.

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.

Abstract: 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.