Package: comandos
ComAnDOSPublicationsA draft is under preparation atcomandos-paper.As a long-time user of SAMap and a connoisseur of cross-species
comparisons, I was always frustrated by the lack of useful visualization
downstream of SAMap results. We don’t need fancy software to figure out
that ciliated cells are similar to ciliated cells, and muscle to muscle,
but what about those pesky “unknown_sensory_2” and “ciliated?_2”
clusters? ComAnDOS is a collection of plotting functions and assorted
utilities that I hacked together to help myself and collaborators make
sense of SAMap results. It is conceived as a SAMap add-on, but it
doesn’t require it per se. It mostly works with two “primitives”:
AnnData, and Pandas DataFrames, so it should be relatively easy to adapt
to your use case.It currently includes:fancy heatmaps (replace Sankey plots, circle plots)paired dotplots (directly compare cross-species expression!)assorted utility functionsBackground and motivationclick to expandSingle-cell RNA-seq (scRNA-seq) is a powerful tool to study the
transcriptome of individual cells. As the technology matured, it became
possible to use it on non-model organisms, facilitating cell type
comparison across species. Early methods for this task subsetted gene
expression matrices to one-to-one orthologous genes, assuming that
sequence conservation also implies conservation of location, magnitude,
and timing of gene expression. Not only is this assumption not true, but
it also requires us to discard a large amount of data.SAMapwas the first method
to try and include many-to-one orthology relations. In alternating
steps, it optimises a cell graph and a gene graph, using the former to
inform the latter and vice versa. The result is a converged
low-dimensional embedding that contains the cells of both species,
allowing for direct comparison.SAMap comes with a small number of visualization tools, but, as I had to
find out myself, they are not sufficient for in-depth analysis. In
particular:Sankey diagrams, SAMap’s default visualization for cluster-cluster
relationships, obscure the fact that cell types are hierarchically
organized. They also make it harder to quantify just how similar two
cell types are according to SAMap.Tarashanskyet al.used network diagrams to demonstrate highly
connected cell type families. I found that these diagrams are not very
informative, as they are hard to read and do not scale well to large
datasets.In the publication, heatmaps are used once, but not to their full
extent.Overlapping dimplots with corresponding violin plots are used to
demonstrate co-expression across species. This is a good idea, but
results in overloaded plots.The authors use dotplots to show gene expression across species,
color-coding the species. This loses one of the dotplots’ dimensions,
where color usually encodes expression level, and forces the use of an
additional axis to show expression magnitude. Furthermore, the
relationships between the plotted genes in the different species are
hard to visualize and need to be described in text.These visualisations have two additional shortcomings: First, they are
not easily reproducible, as they are not part of SAMap but rather custom
solutions for very specific use cases. Second, they are extremely
specific in what they show, and thus not useful for exploratory data
analysis.DocumentationThis package was developed usingnbdev, which
means the source code was generated from Jupyter notebooks using theliterate programming
paradigm. You can
see the exported function signatures and assorted explanationsonline. I am currently working on
tutorials for the most important use cases.For questions or requests please open an issue onGitHub. I will be
communicating updates, if any, onTwitter.Example data is available onZenodo.InstallIt is good practice to set up a virtual environment for your Python
projects. I recommendconda, ormambaif you want faster package
installation.condacreate-ncomandospython=3.9
condaactivatecomandosGitHubFirst install dependencies:pipinstallscanpyjupyterlabAfter installing dependencies, clone the latest version from GitHub and
install it:cd/directory/of/choice
gitclonehttps://github.com/galicae/comandos.gitcdcomandos
pipinstall-e.PyPipipinstallcomandosGetting started<list example notebooks here>