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

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., where this approach is used within the renal context.