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55 lines
2.3 KiB
Markdown
55 lines
2.3 KiB
Markdown
# cellxgene roadmap
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We are very exited for _cellxgene_ to become a valuable tool in collaborations
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between computational biologists and experimental biologists working on
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single-cell transcriptomics data. _cellxgene_ is in active development, and we
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would love to include the community as we plan new features to work on. If you
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have questions of feedback about this roadmap, please submit an issue on
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GitHub.
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Please note: this roadmap is subject to change.
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*Last updated: April 11, 2019*
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## what we are building now
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In the near term, our goal is to enable teams of computational and experimental
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biologists to collaboratively explore and annotate their single-cell RNA-seq data.
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There are 4 key features we plan to implement in the near term.
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- Click install and launch
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- Manual annotation workflows
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- Toggle embeddings
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- Gene information
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### simple install and launch
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The command line interface for installing and launching cellxgene is a barrier
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for users who are not used to Python or using the command line. We plan to
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support installation and launch of cellxgene on Mac and Windows. See
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[Issue #687](https://github.com/chanzuckerberg/cellxgene/issues/687) for more details.
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### manual annotation workflows
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The exploratory visualization that cellxgene offers is critical for manual
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annotation workflows, especially in collaborative environments. We plan to
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support manually annotate cells with labels (i.e., cell type or QC flags) for
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downstream analysis. See [Issue #524](https://github.com/chanzuckerberg/cellxgene/issues/524)
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for more details.
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### toggle embeddings
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While a single dataset may have multiple embeddings calculated (tSNE, umap, in
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situ coordinates, trajectories, etc), cellxgene currently requires the user to select the
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embedding to use in the main layout at launch. We plan to support letting users
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toggle between any embedding present in a file from the cellxgene interface.
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See [Issue #594](https://github.com/chanzuckerberg/cellxgene/issues/594) for details.
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### gene information
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Differential expression returns only the names of genes, but no additional information
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about gene metadata, function, or known associations. We plan to help users learn
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more about genes they discover by exposing additional gene metadata. See
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[Issue #96](https://github.com/chanzuckerberg/cellxgene/issues/96) for details.
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