Spending too much time processing and analysing your flow cytometry data? We have a solution.

Life science and data technology innovator, Aigenpulse, is launching its CytoML Experiment Suite – an automated, end-to-end, machine learning solution specifically aimed at streamlining and automating cytometry analysis at scale and replacing manual gating processes. With it, users will benefit from a single point-of-truth about all cytometry data across any organisation.

CytoML 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%, simultaneously increasing the accuracy, reproducibility, and quality of flow cytometry data.

The Experiment Suite makes it possible to leverage machine learning to scale-up and automate gating using both unsupervised and guided population identification, clearly visualising populations and having full control over gating parameters in the Decision Space. All algorithm parameters are retained for fully transparent and reproducible cytometry gating.

CytoML has been developed from the ground-up to be a validated computerised system aligning to GAMP5. Every analysis, dataset, parameter, and report generated in CytoML is retrievable and reproducible with timestamps, user information, parameters used and data input and output.

It makes it possible for users to parse, integrate and standardise all popular flow cytometry data formats into the system using one seamless process, and import data with an easy-to-use web interface, or via command line or application programming interface (API). Quality assurance reporting is instantly generated during integration, providing full visibility of data quality. The CytoML Experiment Suite provides fully federated and audited logging for processing and integration parameters, enabling re-use and enhancing efficiency.

Insights can easily be derived from exploring the data in different planes using the in-built plotting tools. With reliable gating, events are sorted and annotated into populations which are presented to the user in a hierarchy tree. This empowers the user to select sub-populations for analysis, saving/reloading collections and sharing these with colleagues. Selected sub-populations-to-parent ratios are calculated and visualised, enabling the user to quickly focus on identifying the significant findings from their experiments.

Steve Yemm, Chief Commercial Officer commented: “The clear advantages of the high throughput, multiparameter functionality of flow cytometry are hampered by the immense output of highly complex data. Significant expertise is required to interpret this data correctly and there is a lack of standardisation in assay and instrument set up. Aigenpulse’s CytoML Experiment Suite offers an automated end-to-end process for large numbers of raw files by leveraging machine learning to empower cytometry data processing, and enables users to integrate population counts identified by manual gating to increase the value of data and allow for cross-project analysis.

“This provides significant savings in terms of both time and money and will tackle the all-too-common bottlenecks in the research process, making it possible to maximise the true value of flow cytometry data in pharma R&D.”

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

For more information, please visit https://bit.ly/2FWwDxl.

About Aigenpulse

Aigenpulse Limited was founded in May 2016 to develop the unique Aigenpulse IT platform that takes into account the dynamic and evolving nature of research. With offices in Oxfordshire and London in the UK and Boston in the United States, Aigenpulse consists of experts with PhD level qualifications in Life Sciences combined with highly skilled software developers, bioinformatics data scientists, cloud computing engineers and user experience/user interaction designers.