LAM 0.4.1

Linear Analysis of Midgut

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 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.

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.

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

Acknowledgments

Ville Hietakangas - Hietakangas laboratory

Jaakko Mattila - Mattila laboratory