Metadata-Version: 2.1
Name: clonedetective
Version: 0.0.2
Summary: A simple python package that loads in fluorescence microscopy images and their segmentations, calculates cell properties (e.g. area, mean intensity), and counts cells per clone.
Home-page: https://github.com/morriso1/clonedetective/tree/master/
Author: Otto Morris
Author-email: ottomorris@gmail.com
License: Apache Software License 2.0
Description: # clonedetective
        > A python library for automated cell lineage analysis.
        
        
        <img src="https://raw.githubusercontent.com/morriso1/clonedetective/master/docs/images/clonedetective_scheme.png" width="800" style="max-width: 800px">
        
        ## What does it do?
        
        **clonedetective** is a package for analysing fluorescent imaging data from cell lineage experiments (e.g. FLP-out, MARCM or Cre-lox clones).
        
        Outputted quantifications include:
        - counts of each cell type per “clone”
        - spatial metrics e.g. number of nearest neighbors of each cell per clone
        - cell and clone properties e.g. area, mean intensity etc.
        
        If clones label genetic mutations, these metrics can be useful in addressing biological questions such as:
        - does my gene of interest regulate clone size (i.e. cell proliferation) or clone composition (i.e. cell differentiation)?
        - does the local cell neighbourhood (e.g. number and type of neighbours) non-autonomously impact cell proliferation or differentiation? 
        - does my gene of interest regulate the expression of other (immunostained) proteins?
        
        ## Under the hood
        
        **clonedetective** is constructed using many amazing python libraries, including scikit-image, numpy, Xarray, pandas, numba, Dask, Dask-image and pyclesperanto-prototype.
        
        Many functions are lazy-loaded and parallelized using [Dask](https://dask.org/), enabling clonedetective to scale to large multi-dimensional datasets that do not fit in RAM.
        
        ## Install
        
        It is recommended to install **clonedetective** into a virtual environment e.g. using [conda](https://docs.conda.io/). Once you have anaconda or miniconda installed, you can create a virtual environment using the following command. It is often helpful to install something into an empty environment, in this case we install scipy:
        
        `conda create -n myenv scipy` 
        
        You can then install clonedetective via pip:
        
        `pip install clonedetective`
        
        ## How to use
        
        Please see the tutorials:  
        1) [Example walkthrough](https://morriso1.github.io/clonedetective/Tutorial_Walkthrough.html)  
        2) [Downstream Analysis using Python](https://morriso1.github.io/clonedetective/Tutorial_Downstream_Analysis_Python.html)  
        3) [Downstream Analysis using R](https://morriso1.github.io/clonedetective/Tutorial_Downstream_Analysis_R.html)  
        4) [Generating nuclei segmentation using StarDist](https://morriso1.github.io/clonedetective/Tutorial_StarDist_Segmentation.html)  
        
        In progress, more to come!
        
Keywords: scikit-image,MARCM clones,lineage tracing
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: Apache Software License
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: dev
