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Coexistance of cellfinder+brainreg CLI tool and cellfinder-core workflow
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# BrainGlobe Workflows | ||
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`brainglobe-workflows` is a package that provides users with a number of out-of-the-box data analysis workflows employed in neuroscience, implemented using BrainGlobe tools. | ||
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These workflows represent the most common use-cases and are meant to be easy to reuse. They also serve as an example of how to combine several BrainGlobe tools (possibly together with other tools) to achieve a goal, such as whole brain cell detection and atlas registration. | ||
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You can view the [full documentation for each workflow](https://brainglobe.info/documentation/brainglobe-workflows/index.html) online. | ||
You can also find the documentation for the backend BrainGlobe tools these workflows use [on our website](https://brainglobe.info/). | ||
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At present, the package offers the following workflows: | ||
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- [cellfinder](#cellfinder): Whole-brain detection, registration, and analysis. | ||
At present, the package offers the following workflows to users: | ||
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These workflows should be representative of the most common use-cases and are meant to be easy to reuse. They also serve as an example of how to combine several BrainGlobe tools to achieve a goal, such as whole brain cell detection and atlas registration. | ||
These workflows typically combine several BrainGlobe tools (possibly together with other tools) to achieve a goal, | ||
such as whole brain cell detection and atlas registration. | ||
- [cellfinder](#cellfinder): a command-line tool for whole-brain detection, registration, and analysis. | ||
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## Secondary purposes of brainglobe-workflows, for developers | ||
Additionally, this repository provides functionalities to support code developers. See [Developers documentation](#developers-documentation) for further details. | ||
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We also use these workflows to support code development. We do this by regularly benchmarking the time they take to complete to ensure performance is stable as the code changes. | ||
* Developers can install these benchmarks locally via `pip install .[dev]`. By executing `asv run`, the benchmarks will run with default parameters on a small dataset that is downloaded from [GIN](https://gin.g-node.org/G-Node/info/wiki). See [the asv docs](https://asv.readthedocs.io/en/v0.6.1/using.html#running-benchmarks) for further details on how to run benchmarks. | ||
* Developers can also run these benchmarks on data available locally, by specifying the relevant paths in an input configuration file (JSON file). | ||
* We additionally run and benchmark the workflows locally on a internal desktop machine with large example datasets. These benchmarks are run periodically and the results are made publicly available. | ||
## User documentation | ||
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## Installation | ||
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If you want to install BrainGlobe workflows as a standalone tool, you can run `pip install` in your desired environment: | ||
### Installation of the cellfinder CLI tool | ||
At the moment, users can install the cellfinder CLI tool as a standalone tool, by running `pip install` in your desired environment: | ||
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```bash | ||
pip install brainglobe-workflows | ||
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`brainglobe-workflows` is built using BrainGlobe tools, and it will automatically fetch the tools that it needs and install them into your environment. | ||
Once BrainGlobe version 1 is available, this package will fetch all BrainGlobe tools and handle their install into your environment, to prevent potential conflicts from partial-installs. | ||
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## Contributing | ||
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Contributions to BrainGlobe are more than welcome. | ||
Please see the [developers guide](https://brainglobe.info/developers/index.html). | ||
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## Citing `brainglobe-workflows` | ||
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**If you use any tools in the [brainglobe suite](https://brainglobe.info/documentation/index.html), please [let us know](mailto:[email protected]?subject=cellfinder), and we'd be happy to promote your paper/talk etc.** | ||
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If you find [`cellfinder`](#cellfinder) useful, and use it in your research, please cite the paper outlining the cell detection algorithm: | ||
> Tyson, A. L., Rousseau, C. V., Niedworok, C. J., Keshavarzi, S., Tsitoura, C., Cossell, L., Strom, M. and Margrie, T. W. (2021) “A deep learning algorithm for 3D cell detection in whole mouse brain image datasets’ PLOS Computational Biology, 17(5), e1009074 | ||
[https://doi.org/10.1371/journal.pcbi.1009074](https://doi.org/10.1371/journal.pcbi.1009074) | ||
> | ||
If you use any of the image registration functions in `cellfinder`, please also cite [`brainreg`](https://github.com/brainglobe/brainreg#citing-brainreg). | ||
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--- | ||
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## Cellfinder | ||
### Cellfinder Command Line Interface (CLI) | ||
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Whole-brain cell detection, registration and analysis. | ||
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If you want to just use the cell detection part of `cellfinder`, please see the standalone [cellfinder-core](https://github.com/brainglobe/cellfinder-core) package, or the [cellfinder plugin](https://github.com/brainglobe/cellfinder-napari) for [napari](https://napari.org/). | ||
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`cellfinder` is a collection of tools developed by [Adam Tyson](https://github.com/adamltyson), [Charly Rousseau](https://github.com/crousseau) and [Christian Niedworok](https://github.com/cniedwor) in the [Margrie Lab](https://www.sainsburywellcome.org/web/groups/margrie-lab), generously supported by the [Sainsbury Wellcome Centre](https://www.sainsburywellcome.org/web/). | ||
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`cellfinder` is a designed for the analysis of whole-brain imaging data such as [serial-section imaging](https://sainsburywellcomecentre.github.io/OpenSerialSection/) and lightsheet imaging in cleared tissue. | ||
`cellfinder` is designed for the analysis of whole-brain imaging data such as [serial-section imaging](https://sainsburywellcomecentre.github.io/OpenSerialSection/) and lightsheet imaging in cleared tissue. | ||
The aim is to provide a single solution for: | ||
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- Cell detection (initial cell candidate detection and refinement using deep learning) (using [cellfinder-core](https://github.com/brainglobe/cellfinder-core)), | ||
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``` | ||
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Full documentation can be found [here](https://brainglobe.info/documentation/cellfinder/index.html). | ||
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## Developer documentation | ||
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This repository also includes workflow scripts that are benchmarked to support code development. | ||
These benchmarks are run regularly to ensure performance is stable, as the tools are developed and extended. | ||
* Developers can install these benchmarks locally via `pip install .[dev]`. By executing `asv run`, the benchmarks will run with default parameters on a small dataset that is downloaded from [GIN](https://gin.g-node.org/G-Node/info/wiki). See [the asv docs](https://asv.readthedocs.io/en/v0.6.1/using.html#running-benchmarks) for further details on how to run benchmarks. | ||
* Developers can also run these benchmarks on data they have stored locally, by specifying the relevant paths in an input (JSON) file. | ||
* We also maintain an internal runner that benchmarks the workflows over a large, exemplar dataset, of the scale we expect users to be handling. The result of these benchmarks are made publicly available. | ||
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Contributions to BrainGlobe are more than welcome. | ||
Please see the [developer guide](https://brainglobe.info/developers/index.html). | ||
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## Citing `brainglobe-workflows` | ||
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**If you use any tools in the [brainglobe suite](https://brainglobe.info/documentation/index.html), please [let us know](mailto:[email protected]?subject=cellfinder), and we'd be happy to promote your paper/talk etc.** | ||
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If you find [`cellfinder`](#cellfinder) useful, and use it in your research, please cite the paper outlining the cell detection algorithm: | ||
> Tyson, A. L., Rousseau, C. V., Niedworok, C. J., Keshavarzi, S., Tsitoura, C., Cossell, L., Strom, M. and Margrie, T. W. (2021) “A deep learning algorithm for 3D cell detection in whole mouse brain image datasets’ PLOS Computational Biology, 17(5), e1009074 | ||
[https://doi.org/10.1371/journal.pcbi.1009074](https://doi.org/10.1371/journal.pcbi.1009074) | ||
> | ||
If you use any of the image registration functions in `cellfinder`, please also cite [`brainreg`](https://github.com/brainglobe/brainreg#citing-brainreg). | ||
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--- |
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