This skill provides a rigorous framework for performing quality control on cytometry data. It focuses on identifying and removing acquisition artifacts such as flow-rate instability, signal drift, and dead-cell contamination using specialized R packages.
Use this skill when processing raw cytometry data before downstream analysis. It is critical for ensuring data integrity before performing compensation, transformation, clustering, or differential analysis.
flowAI, PeacoQC, flowCut, and flowClean for anomaly detection.Primarily designed for agents with R execution capabilities or those assisting bioinformatics researchers using the Operon IDE.
This skill has not been reviewed by our automated audit pipeline yet.