UCSC Single Cell Browser
The UCSC Cell Browser is a viewer for single cell data. You can click on and hover over cells to get meta information, search for genes to color on and click clusters to show cluster-specific marker genes.
To look at a list of selected single cell datasets, see http://cells.ucsc.edu
To setup your own cell browser, from Cellranger, Seurat, Scanpy or text files (tsv/csv), or just a single cell expression matrix, read the documentation at http://cellbrowser.rtfd.io
This is a viewer for a static, precomputed layout. If you're looking for an interative layout, where you can move the cells around and run some algorithms interactively, try Chan-Zuckerberg's own cellxgene or Spring. A website with both datasets and some analysis is Scope.
Many labs host their data at cells.ucsc.edu by sending it to us, but some groups have setup their own cell browsers:
- Alexander Misharin Lab, Northwester University, https://www.nupulmonary.org/resources/
- Accelerating Medicine Partnership Consortium, https://immunogenomics.io/cellbrowser/, used in Zhang et al. 2018 and Der et al 2018
- Sansom Lab, Oxford, https://sansomlab.github.io for Croft et al, Nature 2019
- http://caire.ipmc.cnrs.fr/cellbrowser/Differentiation/ (URL has changed, contacted authors, no reply, new URL seems to be https://www.genomique.eu/cellbrowser/HCA/) Zaragosi group at IPMC CNRS Nice, for the manuscript https://dev.biologists.org/content/early/2019/09/25/dev.177428.abstract
- Bin Ren lab, CAAtlas http://catlas.org/mousebrain/#!/home
- Conrad lab at Charite Berlin: http://singlecell.charite.de/
- STAB: a spatio-temporal cell atlas of the human brain from Song et al NAR 2021.
- UCLA: http://mergeomics.research.idre.ucla.edu/PVDSingleCell/
- Lako Lab at Newcastle University, UK: http://retinalstemcellresearch.co.uk/CorneaCellAtlas/ from Collins et al. 2021. The Ocular Surface.
These papers have cell browsers made at UCSC:
- organoidatlas: https://www.sciencedirect.com/science/article/pii/S221112472030053X
- dros-brain: https://elifesciences.org/articles/50354
- kidney-atlas: https://science.sciencemag.org/content/365/6460/1461.abstract
- allen-celltypes/mouse-cortex: https://www.biorxiv.org/content/10.1101/2020.03.30.015214v1.full
- organoidreportcard: https://www.nature.com/articles/s41586-020-1962-0
Before judging this project by the number of issue tickets or PRs, note that at UCSC we use an internal ticket system with more features and that a lot of communication with wetlab users is by email at firstname.lastname@example.org, as we do not require a Github account for feedback. But we do reply to issues here, as you can see from the Github account and also use Github for source control.
- The preferred installation is via pip https://pypi.org/project/cellbrowser/, for documentation see https://cellbrowser.readthedocs.io
- Bioconda: this tool is available to install via bioconda. Note that the conda release is usually a bit outdated relative to the pip release, so use pip if possible. If you cannot use pip, please contact us.
- Biocontainers: there is a biocontainer automatically generated from the bioconda package available here
- The Seurat3Wizard, demo at http://nasqar.abudhabi.nyu.edu/SeuratV3Wizard, builds a cell browser as its last step
- Galaxy: there is a Galaxy tool for UCSC CellBrowser, which can be installed on any Galaxy instance via its Galaxy Toolshed entry or it can be directly used by users at the Human Cell Atlas Galaxy instance or as part of the example workflows, such as the Human Cell Atlas / Scanpy CellBrowser workflow or the EBI Single Cell Expression Atlas / Scanpy / CellBrowser workflow
This project was funded by the California Institute of Regenerative Medicine and the Chan-Zuckerberg Initiative https://www.chanzuckerberg.com/. In 2020, it is funded through a supplement to the NHGRI Genome Browser grant.
This is early research software. You are likely to find bugs. Please open a Github ticket or email us at email@example.com, we can usually fix them quickly.
If you use the UCSC Cell Browser in your work, please cite Speir et al, Biorxiv 2020