Before running CyteType, make sure your data meets these requirements.
Data requirements
- Gene symbols, not Ensembl IDs, in your feature names
- Differential expression results computed per cluster
- Clustering results stored in your object (Leiden, Louvain, or Seurat clusters)
- Normalized gene expression data (log1p-normalization recommended)
Python prerequisites
- A preprocessed AnnData object with
sc.tl.rank_genes_groupsresults - Python ≥ 3.12
R prerequisites
- devtools installed for GitHub installation
- A Seurat object with
FindAllMarkers()output - R ≥ 4.1.0
Supported inputs and species
CyteType expects AnnData (Python/Scanpy) or Seurat (R) objects. Standard Scanpy or Seurat preprocessing workflows do not require reformatting.
Human and mouse datasets are fully supported. Other species including rat and zebrafish can be analysed through ortholog mapping, subject to gene homology quality.
Reference sets optimised for PBMC, brain, liver, kidney, lung, and pancreas are provided. Custom references can also be supplied.
💡 Note: 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.
❓ 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.
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