Comprehensive Integration of Single-Cell Data

Cell, Год журнала: 2019, Номер 177(7), С. 1888 - 1902.e21

Опубликована: Июнь 1, 2019

Язык: Английский

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Integrating single-cell transcriptomic data across different conditions, technologies, and species

Nature Biotechnology, Год журнала: 2018, Номер 36(5), С. 411 - 420

Опубликована: Апрель 2, 2018

Язык: Английский

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SCANPY: large-scale single-cell gene expression data analysis

Genome biology, Год журнала: 2018, Номер 19(1)

Опубликована: Фев. 6, 2018

Scanpy is a scalable toolkit for analyzing single-cell gene expression data. It includes methods preprocessing, visualization, clustering, pseudotime and trajectory inference, differential testing, simulation of regulatory networks. Its Python-based implementation efficiently deals with data sets more than one million cells ( https://github.com/theislab/Scanpy ). Along Scanpy, we present AnnData, generic class handling annotated matrices https://github.com/theislab/anndata

Язык: Английский

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Fast, sensitive and accurate integration of single-cell data with Harmony

Nature Methods, Год журнала: 2019, Номер 16(12), С. 1289 - 1296

Опубликована: Ноя. 18, 2019

Язык: Английский

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GENCODE reference annotation for the human and mouse genomes

Nucleic Acids Research, Год журнала: 2018, Номер 47(D1), С. D766 - D773

Опубликована: Окт. 8, 2018

The accurate identification and description of the genes in human mouse genomes is a fundamental requirement for high quality analysis data informing both genome biology clinical genomics. Over last 15 years, GENCODE consortium has been producing reference gene annotations to provide this foundational resource. includes experimental computational groups who work together improve extend annotation. Specifically, we generate primary data, create bioinformatics tools support expert manual annotators automated annotation pipelines. In addition, workflows use any all publicly available analysis, along with research literature identify characterise loci highest standard. are accessible via Ensembl UCSC Genome Browsers, FTP site, Biomart, Perl REST APIs as well https://www.gencodegenes.org.

Язык: Английский

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Molecular Architecture of the Mouse Nervous System

Cell, Год журнала: 2018, Номер 174(4), С. 999 - 1014.e22

Опубликована: Авг. 1, 2018

The mammalian nervous system executes complex behaviors controlled by specialized, precisely positioned, and interacting cell types. Here, we used RNA sequencing of half a million single cells to create detailed census types in the mouse system. We mapped spatially derived hierarchical, data-driven taxonomy. Neurons were most diverse grouped developmental anatomical units expression neurotransmitters neuropeptides. Neuronal diversity was driven genes encoding identity, synaptic connectivity, neurotransmission, membrane conductance. discovered seven distinct, regionally restricted astrocyte that obeyed boundaries correlated with spatial distribution key glutamate glycine neurotransmitters. In contrast, oligodendrocytes showed loss regional identity followed secondary diversification. resource presented here lays solid foundation for understanding molecular architecture enables genetic manipulation specific

Язык: Английский

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SARS-CoV-2 Receptor ACE2 Is an Interferon-Stimulated Gene in Human Airway Epithelial Cells and Is Detected in Specific Cell Subsets across Tissues

Cell, Год журнала: 2020, Номер 181(5), С. 1016 - 1035.e19

Опубликована: Апрель 27, 2020

There is pressing urgency to understand the pathogenesis of severe acute respiratory syndrome coronavirus clade 2 (SARS-CoV-2), which causes disease COVID-19. SARS-CoV-2 spike (S) protein binds angiotensin-converting enzyme (ACE2), and in concert with host proteases, principally transmembrane serine protease (TMPRSS2), promotes cellular entry. The cell subsets targeted by tissues factors that regulate ACE2 expression remain unknown. Here, we leverage human, non-human primate, mouse single-cell RNA-sequencing (scRNA-seq) datasets across health uncover putative targets among tissue-resident subsets. We identify TMPRSS2 co-expressing cells within lung type II pneumocytes, ileal absorptive enterocytes, nasal goblet secretory cells. Strikingly, discovered a human interferon-stimulated gene (ISG) vitro using airway epithelial extend our findings vivo viral infections. Our data suggest could exploit species-specific interferon-driven upregulation ACE2, tissue-protective mediator during injury, enhance infection.

Язык: Английский

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Opportunities and obstacles for deep learning in biology and medicine

Journal of The Royal Society Interface, Год журнала: 2018, Номер 15(141), С. 20170387 - 20170387

Опубликована: Апрель 1, 2018

Deep learning describes a class of machine algorithms that are capable combining raw inputs into layers intermediate features. These have recently shown impressive results across variety domains. Biology and medicine data-rich disciplines, but the data complex often ill-understood. Hence, deep techniques may be particularly well suited to solve problems these fields. We examine applications biomedical problems-patient classification, fundamental biological processes treatment patients-and discuss whether will able transform tasks or if sphere poses unique challenges. Following from an extensive literature review, we find has yet revolutionize biomedicine definitively resolve any most pressing challenges in field, promising advances been made on prior state art. Even though improvements over previous baselines modest general, recent progress indicates methods provide valuable means for speeding up aiding human investigation. Though linking specific neural network's prediction input features, understanding how users should interpret models make testable hypotheses about system under study remains open challenge. Furthermore, limited amount labelled training presents some domains, as do legal privacy constraints work with sensitive health records. Nonetheless, foresee enabling changes at both bench bedside potential several areas biology medicine.

Язык: Английский

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Deep generative modeling for single-cell transcriptomics

Nature Methods, Год журнала: 2018, Номер 15(12), С. 1053 - 1058

Опубликована: Ноя. 21, 2018

Язык: Английский

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Current best practices in single‐cell RNA‐seq analysis: a tutorial

Molecular Systems Biology, Год журнала: 2019, Номер 15(6)

Опубликована: Июнь 1, 2019

Single-cell RNA-seq has enabled gene expression to be studied at an unprecedented resolution. The promise of this technology is attracting a growing user base for single-cell analysis methods. As more tools are becoming available, it increasingly difficult navigate landscape and produce up-to-date workflow analyse one's data. Here, we detail the steps typical analysis, including pre-processing (quality control, normalization, data correction, feature selection, dimensionality reduction) cell- gene-level downstream analysis. We formulate current best-practice recommendations these based on independent comparison studies. have integrated into workflow, which apply public dataset further illustrate how work in practice. Our documented case study can found https://www.github.com/theislab/single-cell-tutorial This review will serve as tutorial new entrants field, help established users update their pipelines.

Язык: Английский

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