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add --backed info to troubleshooting

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bkmartinjr
2019-11-09 12:59:33 -08:00
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docs/posts/troubleshooting.md
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title: Troubleshooting
description: Troubleshooting
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# Troubleshooting tips & tricks
#### I tried to `pip install cellxgene` and got a weird error I don't understand
@@ -14,8 +15,8 @@ This may happen, especially as we work out bugs in our installation process! Ple
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](https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csc_matrix.html) 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` start time is directly proportional to `h5ad` file size and the speed of your file system. Expect that large (eg, 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 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.
- 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 (eg, 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