Protocol for Identification and Removal of Doublets with DoubletDecon

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Multiplet captures, where two or more cells are captured as a single event, are a common artifact in single-cell RNA sequencing (scRNA-seq).
• These artifacts can lead to misidentification of cell populations and hinder the discovery of novel cell types and marker genes.

Purpose of the Study:

• To present a robust protocol for identifying and removing multiplet captures from scRNA-seq data.
• To introduce DoubletDecon, a computational tool designed to address the challenge of multiplet contamination in scRNA-seq datasets.

Main Methods:

• Development and application of the DoubletDecon algorithm.
• Utilizing a combination of deconvolution methods and unique gene expression analysis to detect putative doublets.
• Providing a detailed protocol for implementing DoubletDecon on scRNA-seq data.

Main Results:

• DoubletDecon effectively identifies and facilitates the removal of multiplet captures from scRNA-seq data.
• Improved identification of distinct and transitional cell populations after doublet removal.
• Enhanced accuracy in discovering cell-type-specific marker genes.

Conclusions:

• DoubletDecon offers a reliable solution for mitigating the impact of multiplet artifacts in scRNA-seq analysis.
• The protocol enables researchers to obtain cleaner datasets, leading to more accurate biological interpretations.
• Accurate cell population and marker gene identification is crucial for advancing single-cell genomics research.