Processing single-cell RNA-seq data for dimension reduction-based analyses using open-source tools.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Single-cell RNA sequencing (scRNA-seq) generates complex data requiring extensive processing for meaningful interpretation.
• Commercial analysis platforms offer convenience but lack the flexibility needed for customized analyses and diverse experimental systems.
• A key challenge in scRNA-seq is distinguishing viable cells from empty droplets, necessitating adaptable computational methods.

Purpose of the Study:

• To develop and demonstrate a flexible, modular data analysis pipeline for scRNA-seq data.
• To address the limitations of inflexible commercial platforms by utilizing open-source software.
• To provide a customizable solution for scRNA-seq data processing, prioritizing pipeline adaptability.

Main Methods:

• Construction of a modular data analysis pipeline using open-source bioinformatics tools.
• Integration of contributed tools to enhance pipeline functionality and flexibility.
• Development of methods for discriminating informative from uninformative cellular material within scRNA-seq data.

Main Results:

• Demonstration of a complete, modular scRNA-seq data analysis pipeline.
• Successful implementation of open-source tools for flexible and customized data processing.
• Establishment of a adaptable framework applicable across various experimental systems.

Conclusions:

• A modular, open-source pipeline offers superior flexibility for scRNA-seq data analysis compared to commercial platforms.
• Pipeline adaptability is crucial for addressing specific experimental needs, such as cell discrimination.
• The presented pipeline provides a robust and customizable solution for researchers in the single-cell genomics field.