Add gget joins the scverse ecosystem blog post#233
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Introduce gget as part of the scverse ecosystem, detailing its functionalities and integration for bioinformatics.
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Updated the description to provide more detail about gget's functionality within the scverse ecosystem.
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| gget was designed with one key idea in mind: one package and import should allow the user to query databases such as Ensembl, UniProt, NCBI, UCSC, AlphaFold, CellxGene, Enrichr, ARCHS4, Open Targets, COSMIC, cBioPortal, and more, without switching to a web browser or writing database-specific boilerplate. | ||
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| Here's what the standard gget installation and workflow looks like: |
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Generally a great section but it doesn't show any output. Is it useful as it is?
| gget enrichr BRCA1 TP53 PTEN ATM --database KEGG_2021_Human | ||
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| ## What does this mean for scverse and gget users? |
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Maybe rephrase this to something like
"gget enables xyz when coupled with scverse tooling" or whatever. Like instead of a question, something concrete. This is not important.
| markers = sc.get.rank_genes_groups_df(adata, group="T cells")["names"].tolist()[:20] | ||
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| # gget.info() requires Ensembl IDs | ||
| # Use gget.search() to resolve symbols to IDs first |
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But this isn't used just below or is it?
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| scverse exists to build a modular, open, and interoperable foundation for single-cell biology. Individual packages focus on what they do best, use shared data structures, and compose naturally with one another. The addition of gget to our ecosystem complements this aspect of our model. This kind of interoperability helps makes tool ecosystems more than the sum of their parts, an important objective for us here at scverse. | ||
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| Someone studying spatial transcriptomics with Squidpy may want to fetch ligand-receptor protein structures from PDB, or pull disease associations from Open Targets, or BLAST a sequence of interest. The integration of gget makes each of those a one-liner by being the layer helping you handle external reference databases alongside those analyses. |
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Squidpy should also be a hyperlink
| pip install gget | ||
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| ```shell |
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I think gget (as a command line tool) would be a good fit for uvx, uv tool install or pipx install.
I think one basically never wants to do uv pip install. It's either uv add to add it to a project, or uv tool install to install it as a global tool.
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Well, yes and no. I understand where you're coming from because gget is mostly a CLI tool but the blog even features a usecase where gget is used as a library in jupyter-like setting to fetch datasets from CELLxGENE. So I wouldn't say "never".
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| The [documentation](https://scverse.org/gget/) covers all modules with worked examples. The [GitHub repository](https://github.com/scverse/gget) is the best place to report issues, propose new database integrations, or contribute. | ||
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| If you are already using scverse tools for single-cell analysis, gget is a natural addition to your environment. And if you are new to the ecosystem, [scverse.org](https://scverse.org) is the place to start. |
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Might want to omit that. A bit fluff.
Consider linking to scverse.org/join in any case
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| --- | ||
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| *gget is an open-source, community-developed tool. Contributions are welcome via the [GitHub repository](https://github.com/scverse/gget).* |
Co-authored-by: Lukas Heumos <lukas.heumos@posteo.net>
Co-authored-by: Lukas Heumos <lukas.heumos@posteo.net>
Co-authored-by: Lukas Heumos <lukas.heumos@posteo.net>
Co-authored-by: Lukas Heumos <lukas.heumos@posteo.net>
Co-authored-by: Lukas Heumos <lukas.heumos@posteo.net>
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Introduce gget as part of the scverse ecosystem, detailing its functionalities and integration for bioinformatics.