An interactive explorer for single-cell transcriptomics data
Quick start
Installation
Gallery
Cellxgene data portal
Demo datasets
Preparing your data
Launching cellxgene
Hosting cellxgene
Annotating data
Methods
Troubleshooting
Roadmap
Contributing (ideas or code)
Extensions
Contact & finding help
Code
pip install cellxgene
and got a weird error I don’t understandThis may happen, especially as we work out bugs in our installation process! Please create a new Github issue, explain what you did, and include all the error messages you saw. It’d also be super helpful if you call pip freeze
and include the full output alongside your issue.
If your dataset requires gigabytes of disk space, you may need to select an appropriate storage format in order to effectively utilize cellxgene
. Tips and tricks:
cellxgene
is optimized for columnar data access. For large datasets, format the expression matrix (.X
) as either a SciPy CSC sparse matrix or a dense Numpy array (whichever creates a smaller h5ad
file). If you are using cellxgene prepare
, include the --sparse
flag to ensure .X
is formatted as a CSC sparse matrix (by default, .X
will be a dense matrix).cellxgene
loads the dataset into memory, and start time is directly proportional to h5ad
file size and the speed of your file system. Expect that large (e.g., million cell) datasets will take minutes to load, even on relatively fast computers with a high performance local hard drive. Once loaded, exploring metadata should still be quick. If this start time is a problem, try the --backed
flag, which will attempt to lazily load data as needed (caveat: subsequent data access may be slower).--disable-diffexp
flag. For datasets that are extremely large, you may also find the --backed
flag improves your ability to explore them.This is likely because you do not have node and npm installed, we recommend using nvm if you’re new to using these tools.