Protocol for interpretable and context-specific single-cell-informed deconvolution of bulk RNA-seq data.

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Single-cell sequencing offers detailed biological insights but faces limitations in clinical utility due to high costs and complex data.
• Bulk RNA sequencing (RNA-seq) is more accessible but lacks single-cell resolution.

Purpose of the Study:

• To develop a cost-effective and accessible method for inferring single-cell information from bulk RNA-seq data.
• To introduce the CLIER algorithm, which leverages pathway-level information to bridge the gap between bulk and single-cell data.

Main Methods:

• A protocol was developed using the pathway-level information extractor (PLIER) algorithm.
• Single-cell signatures were extracted from scientific literature.
• A PLIER model, termed CLIER, was trained using these single-cell signatures.
• The CLIER model was applied to bulk RNA-seq datasets to generate latent variables.

Main Results:

• The CLIER method successfully extracts single-cell-related information from bulk RNA-seq data.
• The resulting latent variables are interpretable within the context of specific single-cell biology.
• This approach offers a way to gain single-cell insights without the need for expensive single-cell sequencing.

Conclusions:

• CLIER provides a valuable tool for researchers and clinicians to derive single-cell insights from readily available bulk RNA-seq data.
• This method enhances the clinical applicability of RNA sequencing by reducing costs and data complexity.
• The protocol facilitates a deeper understanding of cellular heterogeneity and biological processes.