Flow cytometry data analysis is often hindered by technical variance and batch effects across different experiments, leading to unreliable cross-sample comparisons, especially in multi-center studies. Manual gating strategies are time-consuming, subjective, and difficult to standardize, limiting scalability and reproducibility.

Cytolytics offers Cytolution, a cloud-based platform that automates and standardizes flow cytometry data analysis, enabling scientists to derive reliable insights from complex datasets. The platform's pre-optimized algorithms automatically remove technical variance through batch effect correction, compensation, transformation, and cleaning, ensuring accurate cross-sample comparisons. Its Assisted Tree Builder facilitates the creation of gating templates informed by robust statistical insights, automating population identification while maintaining user control. Cytolution's CytoModel employs dimensionality reduction techniques to preserve variance between cell populations, providing high-resolution insights.

Automated pre-processing algorithms for batch effect correction, compensation, and data cleaning

Assisted Tree Builder for creating standardized gating strategies based on statistical insights

CytoModel for dimensionality reduction, preserving variance between cell populations

Cluster Explorer for comparing sample groups and extracting biological insights

Statistics tab for generating customized plots and summary tables

Automated population identification based on marker expression profiles

Panel-agnostic architecture allowing flexibility in fluorophore selection

Option to train custom deep learning models for specific use cases

Cytolytics primarily serves researchers in immunology, oncology, and hematology, as well as core facilities and scientists involved in flow cytometry data analysis.