celltyper

Celltyper is a software tool designed to assign cell type identities to individual cells in single-cell transcriptomic datasets by comparing expression profiles with reference cell-type signatures. It is typically used in single-cell RNA sequencing workflows to move from raw gene expression to biologically interpretable annotations.

The tool accepts input such as an expression matrix (genes by cells), along with optional metadata and a reference atlas or curated marker gene sets. It then computes similarity scores or applies trained classifiers to produce cell-type labels for each cell. Many implementations support preprocessing steps like normalization, dimensionality reduction, and quality filtering as part of the annotation process.

Common approaches used by celltyper include supervised classification based on labeled reference data, probabilistic or fuzzy assignment models, and marker-based scoring that checks the enrichment of known marker genes. Some tools incorporate hierarchical taxonomies (subtypes within broader categories), cross-platform normalization, batch effect correction, and integration of multi-modal data (for example combining transcriptomic and chromatin accessibility signals).

Outputs typically include a per-cell label, a confidence score or probability, and sometimes an assignment path showing how the label was derived. Aggregated results at the cluster level or tissue-wide summaries are also common, enabling downstream analyses such as atlas construction or comparative studies.

Applications and limitations: celltyper is widely used to annotate cell populations in tissue atlases, developmental studies, and disease research, as well as to benchmark analysis pipelines. Its performance depends on the quality and granularity of reference atlases, the presence of novel or rare cell types, and data quality. Users should interpret results with awareness of potential misclassifications and validation requirements.

See also: single-cell RNA sequencing, cell type annotation, reference atlas, marker genes, machine learning in biology.