Besca, a single-cell transcriptomics analysis toolkit to accelerate translational research.
NAR Genom Bioinform
; 3(4): lqab102, 2021 Dec.
Article
em En
| MEDLINE
| ID: mdl-34761219
ABSTRACT
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. Here, we 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, we show how Besca aids acceleration, interoperability, reusability and interpretability of scRNA-seq data analyses, meeting crucial demands in translational research and beyond.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Guideline
Idioma:
En
Revista:
NAR Genom Bioinform
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Suíça