Large-scale arrays of picolitre chambers for single-cell analysis of large cell populations

Lab on a Chip ◽  
2010 ◽  
Vol 10 (21) ◽  
pp. 2952 ◽  
Author(s):  
Won Chul Lee ◽  
Sara Rigante ◽  
Albert P. Pisano ◽  
Frans A. Kuypers
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Single Cell Analysis ◽  
Large Cell ◽  
Cell Populations ◽  
Cell Analysis

scholarly journals Single-Cell Analysis of the Normal Mouse Aorta Reveals Functionally Distinct Endothelial Cell Populations

Circulation ◽  
2019 ◽  
Vol 140 (2) ◽  
pp. 147-163 ◽  
Author(s):  
Aditya S. Kalluri ◽  
Shamsudheen K. Vellarikkal ◽  
Elazer R. Edelman ◽  
Lan Nguyen ◽  
Ayshwarya Subramanian ◽  
...  
Keyword(s):  
Endothelial Cell ◽  
Single Cell ◽  
Normal Mouse ◽  
Single Cell Analysis ◽  
Cell Populations ◽  
Cell Analysis ◽  
Mouse Aorta

scholarly journals Multi-omic single cell analysis resolves novel stromal cell populations in healthy and diseased human tendon

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Adrian R. Kendal ◽  
Thomas Layton ◽  
Hussein Al-Mossawi ◽  
Louise Appleton ◽  
Stephanie Dakin ◽  
...  
Keyword(s):  
Single Cell ◽  
Stromal Cell ◽  
Single Cell Analysis ◽  
Cell Populations ◽  
Cell Analysis ◽  
Human Tendon

scholarly journals Single cell analysis of proliferation and movement of cancer and normal-like cells on nanowire array substrates

2018 ◽  
Vol 6 (43) ◽  
pp. 7042-7049 ◽  
Author(s):  
Zhen Li ◽  
Sofia Kamlund ◽  
Till Ryser ◽  
Mercy Lard ◽  
Stina Oredsson ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Nanowire Array ◽  
Nanowire Arrays ◽  
Cell Populations ◽  
Cell Analysis

Performing single cell analysis can reveal the existence of different cell populations on nanowire arrays.

scholarly journals SIMLR: a tool for large-scale single-cell analysis by multi-kernel learning

2017 ◽  
Author(s):  
Bo Wang ◽  
Daniele Ramazzotti ◽  
Luca De Sano ◽  
Junjie Zhu ◽  
Emma Pierson ◽  
...  
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Single Cell Analysis ◽  
R Package ◽  
Supplementary Information ◽  
Cell Analysis ◽  
Rna Seq ◽  
A Cell ◽  
Supplementary Material ◽  
Public Datasets

AbstractMotivationWe here present SIMLR (Single-cell Interpretation via Multi-kernel LeaRning), an open-source tool that implements a novel framework to learn a cell-to-cell similarity measure from single-cell RNA-seq data. SIMLR can be effectively used to perform tasks such as dimension reduction, clustering, and visualization of heterogeneous populations of cells. SIMLR was benchmarked against state-of-the-art methods for these three tasks on several public datasets, showing it to be scalable and capable of greatly improving clustering performance, as well as providing valuable insights by making the data more interpretable via better a visualization.Availability and ImplementationSIMLR is available on GitHub in both R and MATLAB implementations. Furthermore, it is also available as an R package on [email protected] or [email protected] InformationSupplementary data are available at Bioinformatics online.

scholarly journals scSVAS: CNV clonal visualization online platform for large scale single-cell genomics

2021 ◽  
Author(s):  
Lingxi Chen ◽  
Yuhao Qing ◽  
Ruikang Li ◽  
Chaohui Li ◽  
Hechen Li ◽  
...  
Keyword(s):  
Copy Number Variation ◽  
Single Cell ◽  
Real Time ◽  
Copy Number ◽  
Large Scale ◽  
Single Cell Analysis ◽  
Interactive Visualization ◽  
Cell Analysis ◽  
Single Cell Genomics ◽  
Number Variation

The recent advance of single-cell copy number variation analysis plays an essential role in addressing intra-tumor heterogeneity, identifying tumor subgroups, and restoring tumor evolving trajectories at single-cell scale. Pleasant visualization of copy number analysis results boosts productive scientific exploration, validation, and sharing. Several single-cell analysis figures have the effectiveness of visualizations for understanding single-cell genomics in published articles and software packages. However, they almost lack real-time interaction, and it is hard to reproduce them. Moreover, existing tools are time-consuming and memory-intensive when they reach large-scale single-cell throughputs. We present an online visualization platform, scSVAS, for real-time interactive single-cell genomics data visualization. scSVAS is specifically designed for large-scale single-cell analysis. Compared with other tools, scSVAS manifests the most comprehensive functionalities. After uploading the specified input files, scSVAS deploys the online interactive visualization automatically. Users may make scientific discoveries, share interactive visualization, and download high-quality publication-ready figures. scSVAS provides versatile utilities for managing, investigating, sharing, and publishing single-cell copy number variation profiles. We envision this online platform will expedite the biological understanding of cancer clonal evolution in single-cell resolution. All visualizations are publicly hosted at https://sc.deepomics.org.

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