Find representative subpopulations in single cell imaging data.
Project description
ARCU
Find representative subpopulations in single cell imaging data. ​
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
​​
Installation
Dependencies
- Python >= 3.6, numpy >= 1.22.4, pandas >= 1.3.2
​
You can install the package and necessary dependencies with
pip
by,
pip install arcu
​
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. ​
import pandas
A = pandas.read_csv('dir/file.csv')
​ Then execute ARCU using
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
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