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

Nature Biotechnology, Journal Year: 2018, Volume and Issue: 36(5), P. 411 - 420

Published: April 2, 2018

Language: Английский

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Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage

Nature Immunology, Journal Year: 2019, Volume and Issue: 20(2), P. 163 - 172

Published: Jan. 4, 2019

Language: Английский

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DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors

Cell Systems, Journal Year: 2019, Volume and Issue: 8(4), P. 329 - 337.e4

Published: April 1, 2019

Language: Английский

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Single-cell reconstruction of the early maternal–fetal interface in humans

Nature, Journal Year: 2018, Volume and Issue: 563(7731), P. 347 - 353

Published: Nov. 8, 2018

Language: Английский

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Scrublet: Computational Identification of Cell Doublets in Single-Cell Transcriptomic Data

Cell Systems, Journal Year: 2019, Volume and Issue: 8(4), P. 281 - 291.e9

Published: April 1, 2019

Language: Английский

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

Molecular Systems Biology, Journal Year: 2019, Volume and Issue: 15(6)

Published: June 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.

Language: Английский

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A single-cell atlas of the peripheral immune response in patients with severe COVID-19

Nature Medicine, Journal Year: 2020, Volume and Issue: 26(7), P. 1070 - 1076

Published: June 8, 2020

Language: Английский

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Integrative single-cell analysis

Nature Reviews Genetics, Journal Year: 2019, Volume and Issue: 20(5), P. 257 - 272

Published: Jan. 29, 2019

Language: Английский

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Eleven grand challenges in single-cell data science

Genome biology, Journal Year: 2020, Volume and Issue: 21(1)

Published: Feb. 7, 2020

Abstract The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell technology. Thousands—or even millions—of cells analyzed a single experiment amount to data revolution biology pose unique science problems. Here, we outline eleven challenges that will be central bringing this emerging field of forward. For each challenge, highlight motivating research questions, review prior work, formulate open This compendium is for established researchers, newcomers, students alike, highlighting interesting rewarding problems the coming years.

Language: Английский

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ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis

Nature Genetics, Journal Year: 2021, Volume and Issue: 53(3), P. 403 - 411

Published: Feb. 25, 2021

Abstract The advent of single-cell chromatin accessibility profiling has accelerated the ability to map gene regulatory landscapes but outpaced development scalable software rapidly extract biological meaning from these data. Here we present a suite for analysis in R (ArchR; https://www.archrproject.com/ ) that enables fast and comprehensive ArchR provides an intuitive, user-focused interface complex analyses, including doublet removal, clustering cell type identification, unified peak set generation, cellular trajectory DNA element-to-gene linkage, transcription factor footprinting, mRNA expression level prediction multi-omic integration with RNA sequencing (scRNA-seq). Enabling over 1.2 million single cells within 8 h on standard Unix laptop, is end-to-end will accelerate understanding regulation at resolution individual cells.

Language: Английский

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