Common questions about CyteType. For confidence thresholds and edge-case review habits, see Limits and interpretation.
General
❓ What is CyteType?
CyteType is a structured AI workflow for automated annotation of single-cell RNA-seq data. Specialized steps evaluate marker expression, reference similarity, ontology structure, and literature context in a fixed order, and their outputs are merged into a final annotation with confidence scores and traceable reasoning. The full method is described in the CyteType preprint (Ahuja G et al., bioRxiv 2025).
❓ How is CyteType different from existing tools?
Traditional approaches depend on a single reference or a set of marker genes. CyteType instead integrates several biological signals through a structured agent workflow, leading to higher robustness in rare, transitional, or disease-associated cell populations.
❓ Why is the structured workflow important?
The performance gains originate from the workflow rather than the LLM tier. Each agent contributes a different biological perspective, and a reconciliation step produces a stable, evidence-supported annotation.
❓ Who developed CyteType?
CyteType was created by Nygen Analytics, a research-focused biotech company in Sweden working on AI systems for single-cell omics.
❓ Which programming environments are supported?
CyteType is available in Python for AnnData/Scanpy workflows and in R (CyteTypeR) for Seurat.
❓ How do I install CyteType?
Python: pip install cytetype (Python ≥ 3.12). R: devtools::install_github("NygenAnalytics/CyteTypeR").
❓ Which input formats can I use?
AnnData and Seurat objects. Standard Scanpy or Seurat preprocessing workflows do not require reformatting.
❓ What resources are needed to run CyteType?
CyteType requires internet connectivity for LLM-based annotation. Jobs up to approximately 500,000 cells per request are supported. Larger datasets are automatically batched.
Setup and data
❓ Does CyteType compute marker genes?
CyteType expects user-provided cluster marker genes as priors. Marker selection is often study-specific, so the tool does not override user-defined markers. CyteType supplements these priors by analysing pseudobulked cluster profiles to identify additional genes outside the provided list.
❓ Why do I need to compute markers beforehand?
Marker gene computation is left to the user to maintain compatibility with different preprocessing workflows and clustering strategies. CyteType uses these markers as a structured informational anchor during reasoning.
❓ Why does CyteType pseudobulk clusters?
Pseudobulk profiles allow CyteType to evaluate genes that are not included in user-provided marker lists. This expands the evidence base for annotation and improves accuracy in transitional or poorly characterised populations.
❓ Which species can be annotated?
Human and mouse datasets are fully supported. Other species including rat and zebrafish can be analysed through ortholog mapping, subject to gene homology quality.
❓ Are tissue-specific reference sets included?
Yes. Reference sets optimised for PBMC, brain, liver, kidney, lung, and pancreas are provided. Custom references can also be supplied.
❓ Does CyteType annotate disease samples?
Yes. Because the method does not rely solely on healthy-tissue references, it performs well on tumour samples, perturbed systems, and transitional states.
Analysis
❓ How accurate is CyteType?
CyteType was benchmarked across 20 datasets and 977 clusters. Using the CyteOnto semantic similarity metric against author-assigned labels, CyteType achieved up to 3.8-fold improvement over GPTCellType, 2.68-fold over CellTypist, and 1.01-fold over SingleR. Forty-one percent of clusters received enhanced functional annotation and twenty-nine percent received refined subtype resolution relative to author labels.
❓ How long does annotation take?
Typical runtime is two to three minutes per cluster. A dataset of about fifteen clusters usually completes in thirty to forty-five minutes. The system supports high concurrency for large studies.
❓ Are annotations assigned at the cell or cluster level?
CyteType assigns annotations at the cluster level and propagates them to individual cells. This balances computational efficiency with biological resolution.
❓ Why are repeated runs similar but not identical?
CyteType relies on LLMs, which introduce controlled stochastic variation. Textual reasoning traces may differ across runs, but the method converges to stable biological annotations. Variation occurs at the token level, not at the interpretive level. Archive query.json for full reproducibility records.
❓ How should confidence scores be interpreted?
Confidence values are provided for cell type, subtype, and activation state. Scores above 0.8 generally indicate high reliability. Lower scores mark ambiguous or poorly supported populations that warrant manual review. See Confidence and heterogeneity QC for how to act on mixed or low-confidence clusters.
❓ What does CyteType return?
CyteType adds annotation metadata to the input object, including labels, ontology terms, confidence scores, and evidence summaries. A standalone HTML report provides interactive reasoning, citations, and interpretation tools.
❓ How does Cell Ontology linking work?
CyteType maps annotations to official Cell Ontology (CL) terms. When subtype precision is limited, it proposes the closest parent term and includes ranked alternatives.
❓ How is literature evidence generated?
The Literature and Context agent uses LLM-assisted retrieval to identify relevant publications and summarise supporting evidence. Citations are included for independent verification.
❓ What does anomaly detection identify?
Clusters with unexpected marker signatures are flagged. These can represent doublets, low-quality populations, or potentially novel biological states.
❓ Does the 1000-cell UMAP display affect annotation?
No. UMAP subsampling is purely for rendering. All cells contribute to cluster-level pseudobulk profiles, and annotations are based on full data.
❓ Can CyteType restrict reasoning to specific tissues or lineages?
CyteType does not enforce strict tissue constraints. Reasoning is influenced by the study_context provided by the user, but agents are free to consider the full biological landscape. This avoids prematurely excluding plausible alternatives.
Privacy and security
❓ What data is retained?
Input files and intermediate representations are stored only to support report generation and the report's interactive chat. Nygen does not access or reuse user data for model development.
❓ How long are reports stored?
Reports remain available by default. A report management system is being introduced that will allow users to define retention periods and delete reports directly.
❓ Is user data used for training?
No. User data is not used for training or benchmarking. Only system logs and metadata are monitored to ensure stability.
❓ Are third-party LLM providers involved?
CyteType uses providers such as Anthropic and xAI. Some operate under zero-retention agreements. Nygen does not allow providers to store user data.
❓ Can I delete my report?
Yes. Users may request deletion by providing the job ID to support@nygen.io. Self-service deletion will be available with the new report management backend.
❓ What security infrastructure is used?
CyteType runs on infrastructure built to ISO 27001 and SOC 2 control frameworks. Data is encrypted in transit and at rest, with strict tenant isolation.
❓ Can I use CyteType with sensitive patient data?
For regulated environments, contact contact@nygen.io about enterprise options: on-premises deployment with customer-managed LLMs, isolated storage, and zero data retention policies. The default cloud deployment should not be used with identifiable patient data without explicit data processing agreements.
Pricing and access
❓ Is CyteType free for academic use?
Yes. Academic and non-commercial use is free, with a limit of three annotation runs per day. CyteType is licensed under CC BY-NC-SA 4.0.
❓ How can I exceed the daily limit?
Users may supply their own LLM API keys or run local LLMs. Provider rate limits may constrain concurrency to three to five clusters in parallel.
❓ How is commercial use licensed?
Licensing includes an annual fee and a per-cluster annotation cost. Nygen offers Discovery, Enterprise, and Partnership tiers. Contact contact@nygen.io for details.
❓ Are enterprise deployments available?
Yes. Options include private cloud, AWS Bedrock integration, and on-premises installations supporting air-gapped environments and local LLMs.
Support
❓ Does CyteType fit into existing Scanpy and Seurat workflows?
Yes. CyteType writes outputs directly into object metadata (adata.obs/obj@meta.data). Existing downstream pipelines remain unchanged.
❓ What preprocessing is required?
Standard QC, normalisation, and clustering. Apply batch correction (such as Harmony or Scanorama) when working with multi-batch datasets before submission.
❓ Can I use custom marker lists or references?
Yes. Custom marker genes and reference datasets can be supplied for specialised tissues or perturbation studies.
❓ Does CyteType support multimodal data?
CyteType currently expects transcriptomic input. Support for multimodal and CITE-seq data is under evaluation.
❓ How do I add custom metadata to my report?
Pass metadata as a dict (Python) or named list (R) to the run step. Keys appear as headers in the report. Values that look like URLs are rendered as clickable links, useful for linking GEO accessions, DOIs, or internal data portals.
❓ What if I disagree with an annotation?
The HTML report allows manual override, re-annotation requests, and reasoning queries via the Cluster Copilot. All changes are logged.
❓ How does CyteType address ambiguous populations?
The Reviewer agent highlights ambiguity and provides ranked alternative hypotheses with supporting evidence. Low confidence scores indicate clusters that warrant closer inspection.
❓ My job is taking a long time. Is it stuck?
Jobs continue running on the server even after your local session disconnects. Use annotator.get_results() (Python) or GetResults(obj) (R) to retrieve results after reconnecting. The report URL printed during submission updates live; check it to see per-cluster progress.
❓ The artifact upload is failing. What should I do?
Set require_artifacts=False to skip artifact uploading and proceed with annotation-only mode. Annotation will still complete; the interactive report will lack expression lookups and metadata filtering. For large datasets, load AnnData in backed mode: sc.read_h5ad("file.h5ad", backed="r").
❓ Does CyteType detect doublets or low-quality cells?
Potential doublets and low-quality populations are flagged through confidence scoring. Removal should occur during preprocessing before submission.
❓ Is spatial transcriptomics supported?
Spatial support is in development and will be released after validation.
❓ Can I use a local model with Ollama?
Yes. Expose your local Ollama instance using ngrok, then pass the public URL as baseUrl with provider="openai". The model must support tool calling. See the Ollama integration guide for step-by-step instructions.
❓ How often are databases and references updated?
Cell Ontology updates are synced quarterly. Literature context is refreshed regularly. Workflow improvements are deployed automatically.
❓ How do I cite CyteType in my publication?
Please cite the bioRxiv preprint: Ahuja G et al., Multi-agent AI enables evidence-based cell annotation in single-cell transcriptomics, bioRxiv 2025. doi:10.1101/2025.11.06.686964
❓ Where can I get support?
GitHub: github.com/NygenAnalytics/CyteType, Discord: discord.gg/V6QFM4AN, Support: support@nygen.io
❓ Is there a tutorial or interactive notebook?
Yes. A Google Colab tutorial is available for an interactive end-to-end walkthrough: Open in Colab
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