Demo: scDiagnostics: diagnostic functions to assess the quality of cell type annotations in scRNA-se

scDiagnostics: diagnostic functions to assess the quality of cell type annotations in single-cell RNA-seq data
Annotation transfer from a reference dataset for the cell type annotation of a new query single-cell RNA-sequencing (scRNA-seq) experiment has become an integral component of the typical analysis workflow. The approach provides a fast, automated, and reproducible alternative to the manual annotation of cell clusters based on marker gene expression. However, dataset imbalance and undiagnosed incompatibilities between query and reference dataset can lead to erroneous annotation and distort downstream applications. We present scDiagnostics, an R/Bioconductor package for the systematic evaluation of cell type assignments in scRNA-seq data. scDiagnostics offers a suite of diagnostic functions to assess whether both (query and reference) datasets are aligned, ensuring that annotations can be transferred reliably. scDiagnostics also provides functionality to assess annotation ambiguity, cluster heterogeneity, and marker gene alignment. The implemented functionality helps researchers to determine how accurately cells from a new scRNA-seq experiment can be assigned to known cell types.
Availability: The scDiagnostics package is available from GitHub under github.com/ccb.... A Bioconductor submission is currently in preparation and is planned for May 2024. This timeline should provide sufficient time for package review and inclusion in Bioconductor prior to the conference.
Anthony Christidis