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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Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment

Cell, Journal Year: 2018, Volume and Issue: 174(5), P. 1293 - 1308.e36

Published: June 28, 2018

Language: Английский

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Recovering Gene Interactions from Single-Cell Data Using Data Diffusion

Cell, Journal Year: 2018, Volume and Issue: 174(3), P. 716 - 729.e27

Published: June 28, 2018

Language: Английский

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Single-Cell Transcriptomics of Human and Mouse Lung Cancers Reveals Conserved Myeloid Populations across Individuals and Species

Immunity, Journal Year: 2019, Volume and Issue: 50(5), P. 1317 - 1334.e10

Published: April 9, 2019

Language: Английский

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SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data

GigaScience, Journal Year: 2020, Volume and Issue: 9(12)

Published: Dec. 1, 2020

Abstract Background Droplet-based single-cell RNA sequence analyses assume that all acquired RNAs are endogenous to cells. However, any cell-free contained within the input solution also captured by these assays. This sequencing of constitutes a background contamination confounds biological interpretation transcriptomic data. Results We demonstrate from this "soup" is ubiquitous, with experiment-specific variations in composition and magnitude. present method, SoupX, for quantifying extent estimating "background-corrected" cell expression profiles seamlessly integrate existing downstream analysis tools. Applying method several datasets using multiple droplet technologies, we its application improves otherwise misleading data, as well improving quality control metrics. Conclusions tool removing ambient droplet-based experiments. has broad applicability, can improve utility future datasets.

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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Multiplexed droplet single-cell RNA-sequencing using natural genetic variation

Nature Biotechnology, Journal Year: 2017, Volume and Issue: 36(1), P. 89 - 94

Published: Dec. 11, 2017

Language: Английский

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A single-cell atlas of the airway epithelium reveals the CFTR-rich pulmonary ionocyte

Nature, Journal Year: 2018, Volume and Issue: 560(7718), P. 377 - 381

Published: July 31, 2018

Language: Английский

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Single-cell mapping of gene expression landscapes and lineage in the zebrafish embryo

Science, Journal Year: 2018, Volume and Issue: 360(6392), P. 981 - 987

Published: April 26, 2018

High-throughput mapping of cellular differentiation hierarchies from single-cell data promises to empower systematic interrogations vertebrate development and disease. Here we applied RNA sequencing >92,000 cells zebrafish embryos during the first day development. Using a graph-based approach, mapped cell-state landscape that describes axis patterning, germ layer formation, organogenesis. We tested how clonally related traverse this by developing transposon-based barcoding approach (TracerSeq) for reconstructing lineage histories. Clonally were often restricted state landscape, including case in which two independent lineages converge on similar fates. Cell fates remained lacking chordin gene. provide web-based resources further analysis data.

Language: Английский

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An accurate and robust imputation method scImpute for single-cell RNA-seq data

Nature Communications, Journal Year: 2018, Volume and Issue: 9(1)

Published: March 2, 2018

The emerging single-cell RNA sequencing (scRNA-seq) technologies enable the investigation of transcriptomic landscapes at resolution. ScRNA-seq data analysis is complicated by excess zero counts, so-called dropouts due to low amounts mRNA sequenced within individual cells. We introduce scImpute, a statistical method accurately and robustly impute in scRNA-seq data. scImpute automatically identifies likely dropouts, only perform imputation on these values without introducing new biases rest also detects outlier cells excludes them from imputation. Evaluation based both simulated real human mouse suggests that an effective tool recover transcriptome dynamics masked dropouts. shown identify enhance clustering cell subpopulations, improve accuracy differential expression analysis, aid study gene dynamics.

Language: Английский

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