Metadata-Version: 2.1
Name: rsnaped
Version: 0.0.4
Summary: Python module for single-molecule image processing.
Home-page: https://github.com/MunskyGroup/rsnaped
Author: Luis Aguilera, William Raymond, Brooke Silagy, Brian Munsky
Author-email: luisubald@gmail.com
License: MIT
Description: # rSNAPed
        
        <img src="./docs/images/logo/rSNAPed_Logo.png" width="200" />
        
        rSNAPed : RNA Sequence to NAscent Protein Experiment Designer.
        
        Authors: Luis U. Aguilera, William Raymond, Brooke Silagy, Brian Munsky.
        
        [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
        
        > :warning: **This software is in a very early and experimental stage**: at this point, it is intended to be used for testing and debugging purposes!
        
        ## Description
        
        This library is intended to quantify single-molecule gene expression experiments. Specifically, the code uses [Cellpose](https://github.com/MouseLand/cellpose) to segment the cell in the image. Then, it uses [Trackpy](http://soft-matter.github.io/trackpy/dev/index.html) to detect spots inside the mask. Finally, it uses the spot position to quantify the spot intensity. The code also generates simulated data using [rSNAPsim](https://github.com/MunskyGroup/rSNAPsim). If you use `rSNAPed`, please make sure you properly cite `cellpose`, `trackpy` and `rSNAPsim`.
        
        ## Usage
        
        * Tracking for single-molecule translation (RNA + nascent protein) spots.
        * Tracking for single-molecule RNA spots.
        * RNA detection spots for FISH images.
        * Simulating the single-molecule translation for any gene.
        * Design of single-molecule gene expression experiments.
        
        ## Simulating translation
        
        The code is intended to simulated single-molecule translation. A  video with the simulated cell and a data frame containing spot and intensity positions are generated. This simulation can be used to train new algorithms or for teaching new students.
        
        
        ## Local installation using PIP
        
        * To create a virtual environment using:
        
        ```bash
            conda create -n rsnaped_env python=3.8 -y
            source activate rsnaped_env
        ```
        
        * Open the terminal and use [pip](https://pip.pypa.io/en/stable/) for the installation:
        ```bash
            pip install rsnaped
        ```
        
        ## Local installation from the Github repository
        
        * To create a virtual environment navigate to the location of the requirements file, and use:
        ```bash
            conda create -n rsnaped_env python=3.8 -y
            source activate rsnaped_env
        ```
        * To install GPU for Cellpose (Optional step). Only for **Linux and Windows users** check the specific version for your computer on this [link]( https://pytorch.org/get-started/locally/) :
        ```
            conda install pytorch cudatoolkit=10.2 -c pytorch -y
        ```
        * To install CPU for Cellpose (Optional step). Only for **Mac users** check the specific version for your computer on this [link]( https://pytorch.org/get-started/locally/) :
        ```
            conda install pytorch -c pytorch
        ```
        * To include the rest of requirements use:
        ```
            pip install -r requirements.txt
        ```
        Additional steps to deactivate or remove the environment from the computer:
        * To deactivate the environment use
        ```
            conda deactivate
        ```
        * To remove the environment use:
        ```
            conda env remove -n rsnaped_env
        ```
        
        
        
        ## References for main dependencies
        
        - [rSNAPsim](https://github.com/MunskyGroup/rSNAPsim):
         Aguilera, Luis U., et al. "Computational design and interpretation of single-RNA translation experiments." PLoS computational biology 15.10 (2019): e1007425.
        
        - [Trackpy](http://soft-matter.github.io/trackpy/dev/index.html):
         Dan Allan, et al. (2019, October 16). soft-matter/trackpy: Trackpy v0.4.2 (Version v0.4.2). Zenodo. http://doi.org/10.5281/zenodo.3492186
        
        - [Cellpose](https://github.com/MouseLand/cellpose):
         Stringer, Carsen, et al. "Cellpose: a generalist algorithm for cellular segmentation." Nature Methods 18.1 (2021): 100-106.
        
        ## Licenses for dependencies
        
        **For a complete list containing the complete licenses for the dependencies, check file:  [Licenses_Dependencies.txt](https://github.com/MunskyGroup/rsnaped/blob/master/Licenses_Dependencies.txt).**
        
        - License for [rSNAPsim](https://github.com/MunskyGroup/rSNAPsim): MIT. Copyright © 2018 Dr. Luis Aguilera, William Raymond
        - License for [Trackpy](http://soft-matter.github.io/trackpy/dev/index.html): BSD-3-Clause. Copyright © 2013-2014 trackpy contributors https://github.com/soft-matter/trackpy. All rights reserved.
        - License for [Cellpose](https://github.com/MouseLand/cellpose): BSD 3-Clause. Copyright © 2020 Howard Hughes Medical Institute
        
Keywords: single-molecule image processing
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Image Processing
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.8
Description-Content-Type: text/markdown
