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sc annotation Specifications

Process Details

  • Name: sc annotation
  • Process UUID: c6182143894f46de8a74ac81c43c511f
  • Process Group: SingleCell

Overview

This process performs automated cell type annotation for single-cell RNA sequencing data using ScType methodology. The process identifies and assigns cell type labels to clusters in single-cell datasets based on tissue-specific marker gene databases, enabling researchers to interpret cellular composition and identify distinct cell populations within their samples.

This process is implemented in Bash, which invokes an R script for cell type annotation using the ScType algorithm.

Key Functionality

  • Automated Cell Type Annotation: Assigns cell type labels to single-cell clusters using ScType's marker gene database approach
  • Tissue-Specific Classification: Utilizes tissue-specific marker gene sets to improve annotation accuracy for different organ systems
  • Multi-Organism Support: Supports annotation across different species with organism-specific gene databases
  • Seurat Integration: Works with Seurat objects to maintain compatibility with standard single-cell analysis workflows

Input/Output Specification

Inputs

Required Inputs

  • Single-cell Data Object
    • Description: Processed single-cell RNA-seq data containing clustered cells ready for annotation
    • Format: RDS file (Seurat object)

Outputs

  • Annotated Single-cell Data
    • Description: Single-cell dataset with cell type annotations added to cluster metadata
    • Format: RDS file (annotated Seurat object)

Parameters & Settings

These parameters can be adjusted in the Foundry UI when running this process.

  • Tissue type
    • Description: Select the tissue type of input samples.
    • Available options: Immune system (default), Pancreas, Liver, Eye, Kidney, Brain, Lung, Adrenal, Heart, Intestine, Muscle, Placenta, Spleen, Stomach, Thymus, Hippocampus

References & Resources

  • Tool Documentation: Contact the team for details on run_sctype.R
  • Related Papers: Ianevski, A., Giri, A.K. & Aittokallio, T. Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data. Nat Commun 13, 1246 (2022). https://doi.org/10.1038/s41467-022-28803-w