Troubleshooting tips & tricks

I tried to pip install cellxgene and got a weird error I don’t understand

This 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.

I have a BIG dataset, how can I make cellxgene run as fast as possible?

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).
• By default, 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).
• If your dataset size exceeds the size of memory (RAM) on the host computer, differential expression calculations will be extremely slow (or fail, if you run out of virtual memory). In this case, we recommend running with the --disable-diffexp flag. For datasets that are extremely large, you may also find the --backed flag improves your ability to explore them.

I’m following the developer instructions and get an error about “missing files and directories” when trying to build the client

This is likely because you do not have node and npm installed, we recommend using nvm if you’re new to using these tools.