Journal ArticleDOI
Exponential scaling of single-cell RNA-seq in the past decade.
Valentine Svensson,Valentine Svensson,Roser Vento-Tormo,Sarah A. Teichmann,Sarah A. Teichmann +4 more
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TLDR
In this paper, the authors highlight the key technological developments that have enabled the growth in the data obtained from single-cell RNA-seq experiments, and highlight the advantages of using large numbers of cells.Abstract:
Measurement of the transcriptomes of single cells has been feasible for only a few years, but it has become an extremely popular assay. While many types of analysis can be carried out and various questions can be answered by single-cell RNA-seq, a central focus is the ability to survey the diversity of cell types in a sample. Unbiased and reproducible cataloging of gene expression patterns in distinct cell types requires large numbers of cells. Technological developments and protocol improvements have fueled consistent and exponential increases in the number of cells that can be studied in single-cell RNA-seq analyses. In this Perspective, we highlight the key technological developments that have enabled this growth in the data obtained from single-cell RNA-seq experiments.read more
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Journal ArticleDOI
Comprehensive Integration of Single-Cell Data.
Tim Stuart,Andrew Butler,Paul J. Hoffman,Christoph Hafemeister,Efthymia Papalexi,William M. Mauck,Yuhan Hao,Marlon Stoeckius,Peter Smibert,Rahul Satija +9 more
TL;DR: A strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities.
Journal ArticleDOI
Fast, sensitive and accurate integration of single-cell data with Harmony.
Ilya Korsunsky,Nghia Millard,Jean Fan,Kamil Slowikowski,Fan Zhang,Kevin Wei,Yuriy Baglaenko,Michael B. Brenner,Po-Ru Loh,Po-Ru Loh,Po-Ru Loh,Soumya Raychaudhuri +11 more
TL;DR: Harmony, for the integration of single-cell transcriptomic data, identifies broad and fine-grained populations, scales to large datasets, and can integrate sequencing- and imaging-based data.
Posted ContentDOI
Comprehensive integration of single cell data
Tim Stuart,Andrew Butler,Paul J. Hoffman,Christoph Hafemeister,Efthymia Papalexi,William M. Mauck,Marlon Stoeckius,Peter Smibert,Rahul Satija +8 more
TL;DR: This work presents a strategy for comprehensive integration of single cell data, including the assembly of harmonized references, and the transfer of information across datasets, and demonstrates how anchoring can harmonize in-situ gene expression and scRNA-seq datasets.
Journal ArticleDOI
CellPhoneDB: inferring cell-cell communication from combined expression of multi-subunit ligand-receptor complexes.
TL;DR: The structure and content of CellPhoneDB is outlined, procedures for inferring cell–cell communication networks from single-cell RNA sequencing data are provided and a practical step-by-step guide to help implement the protocol is presented.
Journal ArticleDOI
Single-cell RNA sequencing technologies and bioinformatics pipelines
TL;DR: The available scRNA-seq technologies and the strategies available to analyze the large quantities of data produced will impact both basic and medical science, from illuminating drug resistance in cancer to revealing the complex pathways of cell differentiation during development.
References
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Journal ArticleDOI
Mapping and quantifying mammalian transcriptomes by RNA-Seq.
TL;DR: Although >90% of uniquely mapped reads fell within known exons, the remaining data suggest new and revised gene models, including changed or additional promoters, exons and 3′ untranscribed regions, as well as new candidate microRNA precursors.
Journal Article
Two types of murine helper T cell clone. I. Definition according to profiles of lymphokine activities and secreted proteins.
TL;DR: A panel of antigen-specific mouse helper T cell clones was characterized according to patterns of lymphokine activity production, and two types of T cell were distinguished.
Journal ArticleDOI
Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets
Evan Z. Macosko,Evan Z. Macosko,Anindita Basu,Anindita Basu,Rahul Satija,Rahul Satija,James Nemesh,James Nemesh,Karthik Shekhar,Melissa Goldman,Melissa Goldman,Itay Tirosh,Allison R. Bialas,Nolan Kamitaki,Nolan Kamitaki,Emily M. Martersteck,John J. Trombetta,David A. Weitz,Joshua R. Sanes,Alex K. Shalek,Alex K. Shalek,Alex K. Shalek,Aviv Regev,Aviv Regev,Aviv Regev,Steven A. McCarroll,Steven A. McCarroll +26 more
TL;DR: Drop-seq will accelerate biological discovery by enabling routine transcriptional profiling at single-cell resolution by separating them into nanoliter-sized aqueous droplets, associating a different barcode with each cell's RNAs, and sequencing them all together.
Journal ArticleDOI
Massively parallel digital transcriptional profiling of single cells
Grace X.Y. Zheng,Jessica M. Terry,Phillip Belgrader,Paul Ryvkin,Zachary Bent,Ryan Wilson,Solongo B. Ziraldo,Tobias Daniel Wheeler,Geoffrey P. McDermott,Junjie Zhu,Mark T. Gregory,Joe Shuga,Luz Montesclaros,Jason G. Underwood,Donald A. Masquelier,Stefanie Y. Nishimura,Michael Schnall-Levin,Paul Wyatt,Christopher Hindson,Rajiv Bharadwaj,Alexander Wong,Kevin D. Ness,Lan Beppu,H. Joachim Deeg,Christopher McFarland,Keith R. Loeb,Keith R. Loeb,William J. Valente,William J. Valente,Nolan G. Ericson,Emily A. Stevens,Jerald P. Radich,Tarjei S. Mikkelsen,Benjamin J. Hindson,Jason H. Bielas +34 more
TL;DR: A droplet-based system that enables 3′ mRNA counting of tens of thousands of single cells per sample is described and sequence variation in the transcriptome data is used to determine host and donor chimerism at single-cell resolution from bone marrow mononuclear cells isolated from transplant patients.
Journal ArticleDOI
Clustering by fast search and find of density peaks
Alex Rodriguez,Alessandro Laio +1 more
TL;DR: A method in which the cluster centers are recognized as local density maxima that are far away from any points of higher density, and the algorithm depends only on the relative densities rather than their absolute values.
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