scAVENGERS: a genotype-based deconvolution of individuals in multiplexed single-cell ATAC-seq data without reference...

Area of Science:

• Genomics
• Computational Biology
• Epigenetics

Background:

• Demultiplexing pooled single-cell RNA-seq (scRNA-seq) data relies on genetic differences.
• Existing methods are unsuitable for single-cell ATAC-seq (scATAC-seq) due to lower variant coverage.
• Accurate donor identification is crucial for multiplexed single-cell experiments.

Purpose of the Study:

• To develop a novel computational tool for accurate donor demultiplexing of pooled scATAC-seq data.
• To overcome the limitations of low variant coverage in scATAC-seq data for genotype-based demultiplexing.
• To improve the accuracy and efficiency of analyzing multiplexed scATAC-seq datasets.

Main Methods:

• Introduced scATAC-seq Variant-based EstimatioN for GEnotype ReSolving (scAVENGERS) software.
• Implemented enhanced germline variant calling for individual specificity.
• Utilized an optimized mixture model tailored for scATAC-seq data characteristics.

Main Results:

• scAVENGERS demonstrated superior performance in accuracy and doublet detection compared to existing methods.
• The software achieved a higher proportion of correctly donor-assigned cells in benchmark datasets.
• Analysis provided insights into optimizing pooled single-cell data handling.

Conclusions:

• scAVENGERS effectively resolves donor demultiplexing challenges in pooled scATAC-seq data.
• The developed method enhances the reliability of scATAC-seq analyses with multiple donors.
• The software offers a valuable solution for researchers working with multiplexed scATAC-seq data.