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Data Intelligence Platform Unbiased Analysis Features for Flow Cytometry

Editor: Alexander Stark

Aigenpulse has rolled out an update to its Cyto ML Experiment Suite — its automated, end-to-end, machine learning solution specifically aimed at streamlining and automating cytometry analysis at scale and replacing manual gating processes.

(Source: Public Domain / Pixabay )

Milton/UK — The latest release of Aigenpulse's Suite (v5.2) introduces new unbiased analysis features and has an easy-to-use interface with no need for difficult installation or programme scripting. Users can perform automated analyses in an unbiased manner for exploratory use cases, including Flow SOM and Phenograph for algorithm-based clustering, and use powerful dimensionality reduction methods such as tSNE and UMAP to visualise connected data.

The batch processing tool enables a range of parameters to be simultaneously explored to assist scientists in finding the best representation of their data. Once interesting clusters have been identified, these can be overlaid with marker expression and many types of meta-data to drive hypothesis testing. With the ability to back-gate events from selected clusters into two-dimensions, the new unbiased analysis features streamline the process of assigning identities to populations from clustering outputs ê a traditionally arduous task. To enable comparison and validation of approaches, results can also be compared with semi-automated gating methods.

Unbiased analysis tools allow complex multi-dimensional data to be simplified, unified, processed and visualised so that it can be more easily explored and compared. This kind of analysis can be very useful in exploring data without any prior assumptions, as a means to uncover novel insights. It is a complementary technique to semi-automated approaches and is interoperable within the Cyto ML 5.2 Suite, enabling comparison and validation.

Cyto ML automates every stage of the flow cytometry data lifecycle, from data acquisition to insight generation. It can help increase throughput of data processing and analytics by as much as 600 percent, simultaneously increasing the accuracy, reproducibility, and quality of flow cytometry data. It can be implemented in a GxP environment and, as well as automating processing, the platform enables the reuse of processed cytometry data, integrating population counts identified by manual gating (in .csv format) to increase the value of the data and enable cross-project analysis.

Cyto ML is underpinned by Aigenpulse’s data intelligence platform, which is designed to expedite the drug discovery and development process. The platform harnesses the latest artificial intelligence and machine learning tools to deliver advanced analytics to support scientific decision making.

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