clustifyr: an R package for automated single-cell RNA sequencing cluster classification.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Cell type assignment from single-cell RNA sequencing (scRNA-seq) is critical but often time-consuming and error-prone.
• Existing tools have limitations in the types of reference data they utilize and their data structure flexibility.

Purpose of the Study:

• To develop a versatile and efficient R package, clustifyr, for robust cell-type assignment from scRNA-seq data.
• To enable the use of diverse external data types for improved cell identity prediction.

Main Methods:

• Developed the clustifyr R package, incorporating correlation-based approaches and gene list enrichment methods.
• Benchmarked various parameters to optimize the performance of the cell-type assignment algorithms.
• Enabled integration of multiple reference data types: scRNA-seq, bulk RNA-seq, microarray expression data, and signature gene lists.

Main Results:

• clustifyr provides a lightweight and effective solution for cell-type assignment.
• The package demonstrates compatibility with various scRNA-seq analysis workflows.
• Demonstrated the utility of leveraging multiple external data types for enhanced assignment accuracy.

Conclusions:

• clustifyr offers a flexible and powerful tool to streamline cell-type identification in scRNA-seq studies.
• The package addresses limitations of current methods by supporting diverse reference data inputs.
• Facilitates more accurate and efficient cell-type annotation in genomic research.