Single-cell RNA sequencing (scRNA-seq) revolutionized our understanding of disease biology. The promise it presents to also transform translational research requires highly standardized and robust software workflows. Researchers at Roche present the toolkit Besca, which streamlines scRNA-seq analyses and their use to deconvolute bulk RNA-seq data according to current best practices. Beyond a standard workflow covering quality control, filtering, and clustering, two complementary Besca modules, utilizing hierarchical cell signatures and supervised machine learning, automate cell annotation and provide harmonized nomenclatures. Subsequently, the gene expression profiles can be employed to estimate cell type proportions in bulk transcriptomics data. Using multiple, diverse scRNA-seq datasets, some stemming from highly heterogeneous tumor tissue, the researchers show how Besca aids acceleration, interoperability, reusability and interpretability of scRNA-seq data analyses, meeting crucial demands in translational research and beyond.
Besca provides streamlined single-cell transcriptomics
data analysis modules and exchange file formats
(A) Well-defined interoperable input and output file formats, cluster metrics, a quality control report and a signature storage ensure reusability of data. (B) The standard workflow internalizes a raw count matrix and generates a quality control report as well as a processed dataset post filtering, normalization, highly variable gene selection, batch correction, and clustering. (C, D) Clusters identified from the standard workflow are annotated using either signature-based hierarchical cell annotation (Sig-annot module, C) or a supervised machine learning-based algorithm trained on previously annotated datasets (Auto-annot module, D). (E) The annotated datasets can be used to deconvolute bulk RNA-seq data based on gene expression profiles generated from annotated single-cell datasets utilizing the Bescape module.