BioRetis GmbH
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iCAP - immunoClust Automated Pipeline
AI-Tool for automated Clustering, Identification and Quantification of Flow Cytometry Data
iCAP
Clustering, Population Identification and Quantification
CELL-EVENT clustering
Detection of groups of cells with similar staining characteristics in an individual sample
META-CLUSTER-Annotation
Staining dependent hierarchical sorting and characterization of cell (sub-) populations
META clustering
Matching of cell populations (clusters) with similar staining characteristics between multiple samples
CELL-TYPE Quantification
Absolute and relative quantification of populations by numbers of cells per cluster, variability of markers per cluster and comparison with healthy reference
iCAP
Clustering, Population Identification and Quantification
CELL-EVENT clustering
Detection of groups of cells with similar staining characteristics in an individual sample
META clustering
Matching of cell populations (clusters) with similar staining characteristics between multiple samples
META-CLUSTER-Annotation
Staining dependent hierarchical sorting and characterization of cell (sub-) populations
CELL-TYPE Quantification
Absolute and relative quantification of populations by numbers of cells per cluster, variability of markers per cluster and comparison with healthy reference
Modern adapted cluster algorithms enable reliable and reproducible population detection for flow cytometry data
A co-clustering algorithm with integrated normalization method leads to the detection of stable populations that are comparable across samples
Co-clustering with an analysis model enables the sequential analysis of additional samples for diagnostics
Cloud computing allows for high throughput of many samples
