Metadata-Version: 2.1
Name: arcu
Version: 0.1.1
Summary: Find representative subpopulations in single cell imaging data.
Home-page: https://github.com/harrismdavis/arcu
Author: Harris Davis
Author-email: harris.davis@outlook.com
License: MIT
Description: # ARCU
        Find representative subpopulations in single cell imaging data. 
        â€‹
        ## Introduction
        ARCU is a simple algorithm for finding coordinates in single-cell imaging data where measured features are relatively variable. This is a useful task for finding representative images for publication that illustrate difference in cell types. ARCU finds regions in an image where cells are different; in other words, it finds regions where at least one cell is above a threshold and at least one cell is below a threshold for features of interest. Thresholds are given by:
        
            mu + u*sig
            mu - u*sig
        
        where 
        ```
        mu = mean expression for feature across whole population
        sig = standard deviation for feature across whole population
        u = a scaling coefficent
        ```
        â€‹â€‹
        ## Installation
        Dependencies 
        * Python >= 3.6, numpy >= 1.22.4, pandas >= 1.3.2
        â€‹
        You can install the package and necessary dependencies with `pip` by,
        ```
        pip install arcu
        ```
        â€‹
        ## Example use
        To find regions of interest using ARCU, first read in a pandas dataframe formatted such that the first column is numeric labels, the second is x-coordinates, the third is y-coordinates, and columns 4 through n are features of interest. Rows should be interpretable as "cells" profiled from segmented images with single-cell resolution. 
        â€‹
        ```python
        import pandas
        A = pandas.read_csv('dir/file.csv')
        ```
        â€‹
        Then execute ARCU using
        
        ```python
        import arcu
        centroids = arcu.arcu(A,r,c,u)
        ```
        
        where 
        ```
        Inputs:
          A = dataframe of single cell location and feature data
          r = radius, in pixels, of regions in which to search for subpopulations
          c = the minimum number of cells a region of interest can contain to be considered for reporting
          u = the scaling coefficient on standard deviation for a cell to be considered interesting
        
        Returns:
          a dataframe containing the x,y coordinates of groupings that meet feature expression criteria
        ```
        
Keywords: image analysis,single-cell bioinformatics,computational biology
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
