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
If you want us to add a single cell dataset to the website http://cells.ucsc.edu, please contact us at cells@ucsc.edu. We are happy to add any dataset.
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
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
- 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 Note that the conda release is usually a bit outdated relative to the pip release, so use pip if possible.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 cells@ucsc.edu, we can usually fix them quickly.