Metadata-Version: 2.1
Name: LAM
Version: 0.4.1
Summary: Linear Analysis of Midgut
Home-page: UNKNOWN
Author: Arto I. Viitanen
Author-email: arto.viitanen@helsinki.fi
License: GPL-3.0 License
Project-URL: Project, https://github.com/hietakangas-laboratory/LAM
Project-URL: Bug Reports, https://github.com/hietakangas-laboratory/LAM/issues
Project-URL: Tutorial Videos, https://www.youtube.com/playlist?list=PLjv-8Gzxh3AynUtI3HaahU2oddMbDpgtx
Project-URL: Research Group, https://www.helsinki.fi/en/researchgroups/nutrient-sensing
Description: ![](/img/lam.ico)
        
        # Linear Analysis of Midgut
        ### ---------------LAM---------------
        
        Linear Analysis of Midgut (LAM) is a tool for reducing the dimensionality of microscopy image–obtained data, and for
        subsequent quantification of variables and object counts while preserving spatial context. LAM’s intended use is to
        analyze whole Drosophila melanogaster midguts or their sub-regions for phenotypical variation due to differing
        nutrition, altered genetics, etc. Key functionality is to provide statistical and comparative analysis of variables
        along the whole length of the midgut for multiple sample groups. Additionally, LAM has algorithms for the estimation of
        feature-to-feature nearest distances and for the detection of cell clusters, both of which also retain the regional
        context. LAM also approximates sample widths and can perform multivariate border-region detection on sample groups. The
        analysis is performed after image processing and object detection. Consequently, LAM requires coordinate data of the
        features as input.
        
        ### Installation
        LAM is used in Python >= 3.7 environment. You can install LAM from command line using the 'setup.py' by giving command:
        'python setup.py install' while located inside the LAM-master -directory. Windows-users are recommended to install
        Shapely>=1.7.0 from a pre-compiled wheel found [here](https://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely) in order to
        properly link GEOS and cython.
        
        The distribution also includes docs/requirements.txt and docs/LAMenv.yml that can be used to install dependencies using
        pip or conda (Anaconda), respectively. Recommendation is to install LAM into its own virtual environment.
        
        ### Usage
        LAM is used by executing 'src/run.py', which by default opens up a graphical user interface. If installed through
        setup.py, console command 'lam-run' will also launch LAM. Settings are handled through src/settings.py, but LAM also
        includes argument parsing for most important settings ('python src/run.py -h' OR 'lam-run -h'). Refer to
        'docs/UserManual' for additional information.
        
        A video tutorial series on LAM can be found on YouTube [here](https://www.youtube.com/playlist?list=PLjv-8Gzxh3AynUtI3HaahU2oddMbDpgtx).
        
        Hietakangas lab also provides a stitching script that uses ImageJ to properly stitch images for object detection and
        following LAM analysis. The script can be found [here](https://github.com/hietakangas-laboratory/Stitch).
        
        ### Test data
        The 'data/'-directory includes a small test dataset of two sample groups with four samples each. Note that the
        sample number is not enough for a proper analysis; in ideal circumstances, it is recommended that each sample group
        should have >=10 samples. Refer to user-manual for additional information.
        
        ### License
        This project is licensed under the GPL-3.0 License  - see the LICENSE.md file for details
        
        ### Authors
        Arto I. Viitanen - [Hietakangas laboratory](https://www.helsinki.fi/en/researchgroups/nutrient-sensing)
        
        ### Acknowledgments
        Ville Hietakangas - [Hietakangas laboratory](https://www.helsinki.fi/en/researchgroups/nutrient-sensing/)
        
        Jaakko Mattila - [Mattila laboratory](https://www.helsinki.fi/en/researchgroups/metabolism-and-signaling/)
        
        
Keywords: biology,data analysis,image object data
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3 :: Only
Requires-Python: <3.9,>=3.7
Description-Content-Type: text/markdown
