Metadata-Version: 2.1
Name: pygpseq
Version: 3.3.4
Summary: A GPSeq image analysis package
Home-page: https://github.com/ggirelli/gpseq-img-py
Author: Gabriele Girelli
Author-email: gabriele.girelli@scilifelab.se
License: MIT
Description: pyGPSeq v3.3.4
        ===
        
        A Python3 package that provides tools to analyze images of GPSeq samples.
        
        * Read the (public) [documentation](https://ggirelli.github.io/pygpseq/) for more details.
        * Read the (private) [documentation](https://github.com/ggirelli/pygpseq/wiki) for more details.  
        *Once the repo goes public, private docs will be merged with public ones.*
        
        Installation
        -------------
        
        To **install**, run the following:
        
        ```
        git clone http://github.com/ggirelli/pygpseq
        cd pygpseq
        sudo -H pip3 install .
        ```
        
        To **uninstall** run the following from within the repository folder:
        
        ```
        sudo -H pip3 uninstall pygpseq
        ```
        
        To **update**, first uninstall, and then run the following from within the repository folder.
        
        ```
        git pull
        sudo -H pip3 install .
        ```
        
        Usage
        ----------
        
        #### Analyze a GPSeq image dataset
        
        The `gpseq_anim` (**GPSeq** **an**alysis of **im**ages) analyzes a multi-condition GPSeq image dataset. Run `gpseq_anim -h` for more details.
        
        #### Calculate lamin distance of FISH signals
        
        The `gpseq_fromfish` script characterizes FISH signals identified with `DOTTER` (or similar tools) by calculating: absolute/normalized distance from lamina and central region, nuclear compartment, allele status,... Run `gpseq_fromfish -h` for more details.
        
        #### Merge multiple FISH analyses using a metadata table
        
        Use the `gpseq_fromfish_merge` script to merge multiple FISH analysis output (generated with `gpseq_fromfish`). For more details run `gpseq_fromfish_merge -h`.
        
        #### Perform automatic 3D nuclei segmentation
        
        Run `tiff_auto3dseg -h` for more details on how to produce binary/labeled (compressed) masks of your nuclei staining channels
        
        #### Identify out of focus (OOF) fields of view
        
        Run `tiff_findoof -h` for more details on how to quickly identify out of focus fields of view. Also, the `tiff_plotoof` script (in R, requires `argparser` and `ggplot2`) can be used to produce an informative plot with the signal location over the Z stack.
        
        #### Split a tiff in smaller images
        
        To split a large tiff to smaller square images of size N x N pixels, run `tiff_split input_image output_folder N`. Use the `--enlarge` option to avoid pixel loss. If the input image is a 3D stack, then the output images will be of N x N x N voxels, use the `--2d` to apply the split only to the first slice of the stack. For more details, run `tiff_split -h`.
        
        #### (Un)compress a tiff
        
        To uncompress a set of tiff, use the `tiffcu -u` command. To compress them use the `tiffcu -c` command instead. Use `tiffcu -h` for more details.
        
        #### Convert a nd2 file into single-channel tiff images
        
        Use the `nd2_to_tiff` tool to convert images bundled into a nd2 file into separate single-channel tiff images. Use `nd2_to_tiff -h` for the documentation.
        
        Contributing
        ---
        
        We welcome any contributions to `pygpseq`. Please, refer to the [contribution guidelines](https://ggirelli.github.io/pygpseq/contributing) if this is your first time contributing! Also, check out our [code of conduct](https://ggirelli.github.io/pygpseq/code_of_conduct).
        
        License
        ---
        
        ```
        MIT License
        Copyright (c) 2017 Gabriele Girelli
        ```
Keywords: microscopy image analysis bioimaging biology cell DNA
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3 :: Only
Description-Content-Type: text/markdown
