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    <h1>starfish: scalable pipelines for image-based transcriptomics</h1>

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              <a type="button" class="btn btn-primary" href="https://github.com/spacetx/starfish">github</button></a>
              <a type="button" class="btn btn-primary" href="https://forum.image.sc/tag/starfish">user support</button></a>
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*starfish* is a Python library for processing images of image-based spatial transcriptomics.
It lets you build scalable pipelines that localize and quantify RNA transcripts in image data
generated by any FISH method, from simple RNA single-molecule FISH to combinatorial barcoded
assays. Image processing of an experiment is divided into fields of view (FOV) that correspond to
the data produced by a microscope at a single location on a microscope slide. Each FOV is
processed into a table of spots localized in 3D and then spots are aggregated into a cell x gene
expression matrix by comparing physical positions of spots and cells.

If you want to give it a try, the :ref:`quick start tutorial <quick start>` will guide you from
installation to running a pipeline in under 10 minutes. For more comprehensive instructions on
how to use starfish, see the user guide on :ref:`creating image-based transcriptomics processing
pipelines <user_guide>`, which organizes and contextualizes the
tutorials on running starfish using the API. Finally, advanced users can examine the
:ref:`Data Structures <data structures>` and :ref:`Help & Reference <help and reference>`
sections to learn more details about starfish and its object models.

In addition to the library of image processing functions, starfish introduces a standardized data
format for image-based spatial transcriptomic assays. Examples and tutorials for formatting
your data into the SpaceTx format can be found in :ref:`section_formatting_data`.

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  <h2>Features</h2>

* Formatting Data: :ref:`API<experiment_builder>` | :ref:`Tutorial<section_formatting_data>`
* Registering Images: :ref:`API<learn_transform>` | :ref:`Tutorial<tutorial_image_registration>`
* Filtering Images: :ref:`API<filtering>` | :ref:`Tutorial<section_correcting_images>`
* Finding Spots: :ref:`API<spot_finding>` | :ref:`Tutorial<section_finding_and_decoding>`
* Decoding Spots: :ref:`API<decode_spots>` | :ref:`Tutorial<section_finding_and_decoding>`
* Segmenting Cells: :ref:`API<segmentation>` | :ref:`Tutorial<section_segmenting_cells>`
* Assigning Spots: :ref:`API<target_assignment>` | :ref:`Tutorial<tutorial_assigning_spots>`
* Example Pipelines: :ref:`Gallery<pipeline_examples>`

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  <h2>Contents</h2>

.. toctree::
   :maxdepth: 1

   installation/index
   user_guide/index
   gallery/index
   help_and_reference/index
   api/index
   developer_guide/index
   about/index

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