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

TL;DR

This paper introduces a protocol to extract single-cell insights from bulk RNA-seq data using the PLIER algorithm, making single-cell information more accessible and interpretable.

Contribution

The paper presents a novel protocol for single-cell-informed deconvolution of bulk RNA-seq data using a modified PLIER algorithm called CLIER.

Findings

The protocol enables the extraction of interpretable latent variables from bulk RNA-seq data based on single-cell signatures.

The CLIER model is trained using single-cell signatures from literature and applied to new datasets for context-specific analysis.

Abstract

Single-cell sequencing provides rich information; however, its clinical use is limited due to high costs and complex data output. Here, we present a protocol for extracting single-cell-related information from bulk RNA-sequencing (RNA-seq) data using the pathway-level information extractor (PLIER) algorithm. We describe the steps for extracting single-cell signatures from literature, training a PLIER model based on single-cell signatures (named CLIER), and applying it to a new dataset. This produces latent variables that are interpretable in the context of specific single-cell biology.

For complete details on the use and execution of this protocol, please refer to Legouis et al.,1 where this approach is used within the renal context.

•Guidelines for constructing a single-cell signature atlas•Procedure for learning a single-cell-informed deconvolution specific to a given context•Steps for using the learned deconvolution to transform a new dataset•Instructions for possible analyses that can be performed on the transformed dataset

Guidelines for constructing a single-cell signature atlas

Procedure for learning a single-cell-informed deconvolution specific to a given context

Steps for using the learned deconvolution to transform a new dataset

Instructions for possible analyses that can be performed on the transformed dataset

Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Single-cell sequencing provides rich information; however, its clinical use is limited due to high costs and complex data output. Here, we present a protocol for extracting single-cell-related information from bulk RNA-sequencing (RNA-seq) data using the pathway-level information extractor (PLIER) algorithm. We describe the steps for extracting single-cell signatures from literature, training a PLIER model based on single-cell signatures (named CLIER), and applying it to a new dataset. This produces latent variables that are interpretable in the context of specific single-cell biology.

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