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With new unbiased analysis features and an easy-to-use interface, the CytoML 5.2 uses batch processing to engage a range of parameters used simultaneously for better data representation.
Aigenpulse launched the CytoML 5.2, an update to its CytoML Experiment Suite, an automated machine learning solution aimed at streamlining and automating cytometry analysis at scale and replacing manual gating processes.
The new addition works to automate every stage in the flow cytometry data lifecycle by increasing the throughput of data processing and analytics by as much as 600%, increasing the accuracy, reproducibility, and quality of flow cytometry data. The new system also enables the reuse of processed cytometry data, the integration of population counts identified by manual gating to increase the value of the data, and allowing for cross-project analysis.
With new unbiased analysis features and an easy-to-use interface, the CytoML 5.2 uses a batch processing tool to engage a range of parameters used simultaneously to aid scientists in finding the best representation of their data. After significant clusters have been pinpointed, these clusters can be overlaid with marker expression and many types of meta-data to drive hypothesis testing. Meanwhile, the unbiased analysis features simplify the process of assigning identities to populations from clustering outputs.
“Unbiased analysis tools allow complex multi-dimensional data to be simplified, unified, processed, and visualized so that it can be more easily explored and compared,” said Satnam Surae, chief product officer at Aigenpulse, in an April 27, 2021 company press release. “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 CytoML 5.2 Suite, enabling comparison and validation.”