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A marker gene-based method for identifying the cell-type of origin from single-cell RNA sequencing data
Nima Nouri1, Giorgio Gaglia1, Andre H Kurlovs1
1Precision Medicine and Computational Biology, Sanofi, 350 Water Street, Cambridge, MA 02141, USA.
Methodsx
|July 10, 2023
Summary
Sargent rapidly identifies cell types in single-cell RNA sequencing (scRNA-seq) data using cell-specific markers. This transformation-free, cluster-free algorithm ensures accurate, reproducible, and scalable cell annotation for biological insights.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Single-cell RNA sequencing (scRNA-seq) offers high-resolution tissue analysis.
- Accurate cell type identification is crucial for interpreting scRNA-seq data.
- Manual annotation is labor-intensive and prone to bias.
Purpose of the Study:
- To develop a rapid, accurate, and automated algorithm for single-cell annotation.
- To overcome limitations of existing cell identification methods.
- To improve the scalability and reproducibility of scRNA-seq data analysis.
Main Methods:
- Introduced Sargent, a transformation-free and cluster-free annotation algorithm.
- Utilized cell type-specific markers for identification.
- Validated performance on simulated and expert-annotated human organ datasets (PBMC, heart, kidney, lung).
Main Results:
- Sargent demonstrated high accuracy in annotating simulated datasets.
- Performance was comparable to expert annotations on real-world human organ data.
- The algorithm proved flexible and biologically interpretable, similar to manual methods.
Conclusions:
- Sargent provides a robust, reproducible, and scalable solution for automated cell type annotation.
- It enhances the efficiency and reliability of downstream scRNA-seq analyses.
- The method preserves biological interpretability while reducing manual effort and bias.
