The intelligence layer
for your
single-cell omics data
See how your team can go from raw clusters to deep biological characterization in days.
Explore CyteTypeDeep characterization in every report
Ontology-mapped labels
Every cluster linked to a Cell Ontology term with confidence scores
Functional state programs
Activation, exhaustion, and transition states resolved at program level
Pathway-level evidence
GO and WikiPathways enrichment with ranked NES values per cluster
Audit-ready documentation
Full evidence trails, reviewer rationale, and exportable annotation tables
Used by teams at
What changes when annotation takes days instead of weeks
Weeks to days
Compress annotation timelines across your pipeline. Over 100,000 clusters annotated with 99.99% completion rate.
Audit-ready evidence
Ontology IDs, marker-level evidence trails, and multi-reviewer rationale on every call. Ready for regulatory review.
Built for regulated data
On-prem deployment with pharma-run LLMs, zero data retention, no model training on your data, and isolated storage.
Fits your existing stack
Works with Scanpy, Seurat, and AnnData via the CyteType Python and R packages. No infrastructure changes.
Where CyteType delivers
Site-to-site labeling drift makes consortium atlases hard to align and harder to defend. Ontology-mapped calls with explicit evidence and confidence scoring enforce one reviewable language across studies.
Potency and failure risk often sit in functional states that coarse labels miss. Program-level state resolution highlights exhaustion, activation, and transition signals with marker-level support.
Static labels flatten tumor-immune dynamics and hide clinically relevant states. Multi-agent annotation resolves immune context, functional programs, and competing label hypotheses per cluster.
Off-target populations and stress programs are easy to miss until late-stage review. Evidence-linked annotation flags inflammatory and atypical programs with confidence and citation trails.
Inconsistent cohort annotation blurs signals tied to response and resistance. Harmonized labels plus pathway-ranked evidence expose reproducible state programs across cohorts.
Longitudinal immune shifts are difficult to compare when baseline and on-treatment calls drift. Consistent annotation with traceable rationale preserves comparability across timepoints and cohorts.
What's in a CyteType report
Ontology-anchored annotation
Each cluster mapped to a Cell Ontology term with confidence and label match scores.
Functional state resolution
Program-level gene sets separate markers by function instead of a single flat list.
Marker-level evidence
Supporting, missing, and unexpected genes with biological context and linked citations.
Confidence and heterogeneity QC
Badges summarize certainty and heterogeneity with narrative reasoning per cluster.
Decision traceability
Full candidate funnel showing why each label was accepted or rejected.
Audit-ready export
Export cluster IDs, CL terms, states, confidence scores, and label match percentages.
In translational research, you need annotations that can withstand scrutiny. The complete evidence trail — markers, citations, reviewer assessments — provides documentation we haven't had access to before. We've used it for immune monitoring studies, and the clinical team appreciated being able to trace the reasoning behind each classification.
Schedule your CyteType demo
Bring a dataset and walk away with a full characterization report. Our team will show you how CyteType fits into your workflow.
Demo slots fill quickly. Pick a time that works for your team.
Learn more about CyteType