Dataset Information

Besca, a single-cell transcriptomics analysis toolkit to accelerate translational research.

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.

SUBMITTER: Madler SC

PROVIDER: S-EPMC8573822 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Besca, a single-cell transcriptomics analysis toolkit to accelerate translational research.

Mädler Sophia Clara SC Julien-Laferriere Alice A Wyss Luis L Phan Miroslav M Sonrel Anthony A Kang Albert S W ASW Ulrich Eric E Schmucki Roland R Zhang Jitao David JD Ebeling Martin M Badi Laura L Kam-Thong Tony T Schwalie Petra C PC Hatje Klas K

NAR genomics and bioinformatics 20211108 4

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 <i>Besca</i>, 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 <i>Besca</i> modules, ...[more]

PMID: 34761219

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Project description:There is a great deal of interest in personalized, individualized, or precision interventions for disease and health-risk mitigation. This is as true of nutrition-based intervention and prevention strategies as it is for pharmacotherapies and pharmaceutical-oriented prevention strategies. Essentially, technological breakthroughs have enabled researchers to probe an individual's unique genetic, biochemical, physiological, behavioral, and exposure profile, allowing them to identify very specific and often nuanced factors that an individual might possess, which may make it more or less likely that he or she responds favorably to a particular intervention (e.g., nutrient supplementation) or disease prevention strategy (e.g., specific diet). However, as compelling and intuitive as personalized nutrition might be in the current era in which data-intensive biomedical characterization of individuals is possible, appropriately and objectively vetting personalized nutrition strategies is not trivial and requires novel study designs and data analytical methods. These designs and methods must consider a very integrated use of the multiple contemporary biomedical assays and technologies that motivate them, which adds to their complexity. Single-subject or N-of-1 trials can be used to assess the utility of personalized interventions and, in addition, can be crafted in such a way as to accommodate the necessarily integrated use of many emerging biomedical technologies and assays. In this review, we consider the motivation, design, and implementation of N-of-1 trials in translational nutrition research that are meant to assess the utility of personalized nutritional strategies. We provide a number of example studies, discuss appropriate analytical methods given the complex data they generate and require, and consider how such studies could leverage integration of various biomarker assays and clinical end points. Importantly, we also consider the development of strategies and algorithms for matching nutritional needs to individual biomedical profiles and the issues surrounding them. Finally, we discuss the limitations of personalized nutrition studies, possible extensions of N-of-1 nutritional intervention studies, and areas of future research.

Dataset Information

Besca, a single-cell transcriptomics analysis toolkit to accelerate translational research.

Publications

Besca, a single-cell transcriptomics analysis toolkit to accelerate translational research.

Similar Datasets

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