Analytical Workflows for Single-Cell Multiomic Data Using the BD Rhapsody Platform.

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• High-throughput single-cell RNA sequencing (scRNA-seq) generates vast amounts of data.
• Extracting meaningful biological insights requires robust data quality and noise reduction.

Purpose of the Study:

• To describe a comprehensive analytical workflow for single-cell multiomics data.
• To enable high-quality data analysis and biological interpretation from scRNA-seq experiments.

Main Methods:

• The workflow includes six distinct pipelines covering image analysis, quality control, and matrix generation.
• It details gene expression matrix pre-processing, advanced analysis, and conversion to flow cytometry standard (FCS) format.
• Visualization techniques using graphical interfaces are also included.

Main Results:

• The described workflow facilitates the conversion of raw sequencing reads into biologically relevant data.
• It enables effective separation of biological signal from noise in complex scRNA-seq datasets.
• The methodology supports high-quality data analysis for single-cell multiomics.

Conclusions:

• This detailed analytical workflow provides a standardized approach for scRNA-seq data analysis.
• It empowers researchers to derive significant biological insights from single-cell multiomics studies.
• The described pipelines ensure data integrity and facilitate downstream interpretation.