Cytolytics is a data analysis platform that specializes in cell population analysis, providing tools for batch effect correction and statistical analysis. The platform enables researchers to identify and correct for inconsistencies in cell data across different experiments, improving the accuracy and reliability of research results.
Funding
Funding not disclosed
Founders
Product
Problem
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.
Solution
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.
Target Audience
Cytolytics primarily serves researchers in immunology, oncology, and hematology, as well as core facilities and scientists involved in flow cytometry data analysis.
Features
- 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