Dependency issue: In Conda, with SCENIC

I’ve been working with pySCENIC in the recent couples of months. It’s a tool that is meant to uncover gene sets that are controlled by transcription factors and suggests the underlying logic of how genes are regulated. It takes gene expression data from single-cell RNA sequencing and seems to be fairly easy to use.

So, what took me several months? I did my preliminary run with their R implementaion, which was released earlier in the original reference. It was well-documented and ran smoothly over a set of 10,000 cells. Nonetheless, it did not scale well. With 100,000 cells, it could take a month to run. The authors are very aware of this and kindly re-implemented it in Python, which is approximately more efficient by two orders of magnitude in my case.

Everything should be smooth in theory, but in practice, I encountered a dependency issue at the first step when I pip installed the package.

After finding a way out of the compilation issue, I experienced repeated segmentation faults running the analysis. Since it ran successfully once, I thought that it was something wrong with my main analysis, but it turned out to be that later versions of dask is not compatible with the package itself.

It was then I painfully learned that I should really manage my analysis with virtual environments. I found the authors provided a YAML environment file in the repository, so I tried to use it. conda complained. It said that mkl-random was not compatible with openblas, so it could do nothing here.

I played with the versions of packages a little bit and was able to run my analysis with a bunch of warning messages, and after two weeks, after I tried to enable jupyter notebook in the environment, the dependency was broken again.

I started over, but I forgot what I did to the versions so I failed to restore a setting that enables my analysis. Then I came across a thread at StackOverflow saying numpy from the default channel was somehow creating a mess with openblas and mkl. Per the suggestion in the above therad, I added a line in the YAML, prioritizing conda-forge over defaults, and magically the original environment file from the authors runs without any additional adjustments.

The morale of the story:

  1. Dependencies should always be managed and documented when possible. It’s much harder, if not possible, to trace it from error messages. I was able to find dask causing segmentation faults was sheer luck when I tried to start everything over with a clean environment and finally opted in to use conda.

  2. Even with dependencies provided (like a conda environment file), there could be local settings that was not in it. In my case, the authors could have set conda-forge as the preferred channel in $HOME/.condarc and forgot about it.

  3. Maybe I should create a dependency tag because that seems like the main theme of this blog, right?

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Yen-Chung Chen
PhD Student

Yen is a graduate student interested in developmental biology, neurobiology and bioinformatics.

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