cellxgene/docs/fag.md

layout, title, description

layout	title	description
default	FAQ	Frequently Asked Questions

data formatting

Someone sent me a directory of 10X-Genomics data with a mtx file and I've never used scanpy, can I use cellxgene?

Yep! This should only take a couple steps. We'll assume your data is in a folder called data/ and you've successfully installed cellxgene with the louvain packages as described above. Just run

cellxgene prepare data/ --output=data-processed.h5ad --layout=umap

Depending on the size of the dataset, this may take some time. Once it's done, call

cellxgene launch data-processed.h5ad --layout=umap --open

And your web browser should open with an interactive view of your data.

In my prepare command I received the following error Warning: louvain module is not installed, no clusters will be calculated. To fix this please install cellxgene with the optional feature louvain enabled

Louvain clustering requires additional dependencies that are somewhat complex, so we don't include them by default. For now, you need to specify that you want these packages by using

pip install cellxgene[louvain]

I ran prepare and I'm getting results that look unexpected

You might want to try running one of the preprocessing recipes included with scanpy (read more about them here). You can specify this with the --recipe option, such as

cellxgene prepare data/ --output=data-processed.h5ad --recipe=zheng17

It should be easy to run prepare then call cellxgene launch a few times with different settings to explore different behaviors. We may explore adding other preprocessing options in the future.

I have extra metadata that I want to add to my dataset

Currently this is not supported directly, but you should be able to do this manually using scanpy. For example, this notebook shows adding the contents of a csv file with metadata to an anndata object. For now, you could do this manually on your data in the same way and then save out the result before loading into cellxgene.

What part of the anndata objects does cellxgene pull in for visualization?

• .obs and .var annotations are use to extract metadata for filtering
• .X is used to display expression (histograms, scatterplot & colorscale) and to compute differential expression
• .obsm is used for layout

When I start cellxgene, I get an error Unexpected HTTP response 500, INTERNAL SERVER ERROR -- Out of range float values are not JSON compliant in the web UI, or Warning: JSON encoding failure - suggest trying --nan-to-num command line option in the CLI. What can I do?

At the moment, cellxgene is unable to transmit floating point NaN or Infinity values to the web UI (due to a limitation on data serialization method in use). We expect to resolve this in a future release, but in the meantime, you can work around this issue by starting cellxgene with the --nan-to-num command line option, ie, cellxgene launch data.h5ad --nan-to-num.

This option will convert all NaNs to zero, and all positive/negative infinities to the min/max of the data element within which the value was found (eg, +Infinity within an obs annotation will be converted to the maximum finite value in that annotation). This option will increase startup time, so we recommend only using it when the dataset contains NaN/Infinities.

installing and building

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'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.

algorithms

How are you computing and sorting differential expression results?

We use a Welch's t-test implementation including the same variance overestimation correction as used in scanpy. We sort the tscore to identify the top N genes, and then filter to remove any that fall below a cutoff log fold change value, which can help remove spurious test results. The default threshold is 0.01 and can be changed using the option --diffexp-lfc-cutoff.