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# Cyto utilities | ||
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Functions enabling smooth interaction with CellProfiler and DeepProfiler output formats. | ||
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::: pycytominer.cyto_utils |
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# Main Functions | ||
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<!-- prettier-ignore-start --> | ||
<!-- mkdocs block --> | ||
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::: pycytominer | ||
options: | ||
members: | ||
- aggregate | ||
- annotate | ||
- consensus | ||
- feature_select | ||
- normalize | ||
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<!-- mkdocs block END --> | ||
<!-- prettier-ignore-end --> |
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{% | ||
include-markdown "../README.md" | ||
%} |
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Install | ||
======= | ||
# Installation | ||
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To install pycytominer, use pip: | ||
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.. code-block:: bash | ||
```bash | ||
pip install pycytominer | ||
``` | ||
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You can also install pycytominer with conda: | ||
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.. code-block:: bash | ||
```bash | ||
conda install -c conda-forge pycytominer | ||
``` |
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# Operations | ||
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We do not recommend interacting with these functions directly. | ||
The core pycytominer API uses these operations internally. | ||
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::: pycytominer.operations |
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# Tutorials | ||
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`This <https://github.com/cytomining/pipeline-examples#readme>`\_ tutorial shows how to run a image-based profiling pipeline using pycytominer. Using IPython notebooks, it walks through the following steps: | ||
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#. Downloading a dataset of single cell `CellProfiler <https://cellprofiler.org/>`_ profiles. | ||
#. Processing the profiles using PyCytominer. This includes the following steps: | ||
#. Data initialization | ||
#. Single cell aggregation to create well-level profiles | ||
#. Addition of experiment metadata to the well-level profiles | ||
#. Profile normalization | ||
#. Feature selection | ||
#. Forming consensus signatures | ||
#. Evaluating the profile quality using `cytominer-eval <https://github.com/cytomining/cytominer-eval>`_. |
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# Walkthroughs | ||
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.. toctree:: | ||
:maxdepth: 1 | ||
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walkthroughs/single_cell_usage.ipynb |
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