CellSIUS provides sensitive and specific detection of rare cell populations from complex single-cell RNA-seq data-Reference-Cited by-同舟云学术

CellSIUS provides sensitive and specific detection of rare cell populations from complex single-cell RNA-seq data

Published:2019-07-17 Issue:1 Volume:20 Page:
ISSN:1474-760X
Container-title:Genome Biology
language:en
Short-container-title:Genome Biol

Author:

Wegmann Rebekka,Neri Marilisa,Schuierer Sven,Bilican Bilada,Hartkopf Huyen,Nigsch Florian,Mapa Felipa,Waldt Annick,Cuttat Rachel,Salick Max R.,Raymond Joe,Kaykas Ajamete,Roma Guglielmo,Keller Caroline Gubser^ORCID

Publisher

Springer Science and Business Media LLC

Link

http://link.springer.com/content/pdf/10.1186/s13059-019-1739-7.pdf

Reference74 articles.

1. Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M, et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell. 2015;161:1202–14 Available from: https://doi.org/10.1016/j.cell.2015.05.002 .

2. Klein AM, Mazutis L, Akartuna I, Tallapragada N, Veres A, Li V, et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell. 2015;161:1187–201 Available from: https://doi.org/10.1016/j.cell.2015.04.044 .

3. Zheng GXY, Terry JM, Belgrader P, Ryvkin P, Bent ZW, Wilson R, et al. Massively parallel digital transcriptional profiling of single cells. Nat Commun. 2017;8:14049 Available from: https://doi.org/10.1038/ncomms14049 .

4. Svensson V, Vento-Tormo R, Teichmann SA. Exponential scaling of single-cell RNA-seq in the last decade. Nature Protocols. 2018;13:599–604 Available from: https://doi.org/10.1038/nprot.2017.149 .

5. Tang F, Barbacioru C, Wang Y, Nordman E, Lee C, Xu N, et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods. 2009;6:377–82 Available from: https://doi.org/10.1038/nmeth.1315 .

Cited by 45 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. scSID: A lightweight algorithm for identifying rare cell types by capturing differential expression from single-cell sequencing data;Computational and Structural Biotechnology Journal;2024-12

2. scCAD: Cluster decomposition-based anomaly detection for rare cell identification in single-cell expression data;Nature Communications;2024-08-31

3. Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics;Nature Reviews Molecular Cell Biology;2024-08-21

4. Connectivity Network Feature Sharing in Single-Cell RNA Sequencing Data Identifies Rare Cells;Journal of Chemical Information and Modeling;2024-08-03

5. Mining alternative splicing patterns in scRNA-seq data using scASfind;Genome Biology;2024-07-29