# 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.
​
```python
import pandas
A = pandas.read_csv('dir/file.csv')
```
​
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
```
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"description": "# ARCU\nFind representative subpopulations in single cell imaging data. \n\u00e2\u20ac\u2039\n## Introduction\nARCU 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:\n\n mu + u*sig\n mu - u*sig\n\nwhere \n```\nmu = mean expression for feature across whole population\nsig = standard deviation for feature across whole population\nu = a scaling coefficent\n```\n\u00e2\u20ac\u2039\u00e2\u20ac\u2039\n## Installation\nDependencies \n* Python >= 3.6, numpy >= 1.22.4, pandas >= 1.3.2\n\u00e2\u20ac\u2039\nYou can install the package and necessary dependencies with `pip` by,\n```\npip install arcu\n```\n\u00e2\u20ac\u2039\n## Example use\nTo 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. \n\u00e2\u20ac\u2039\n```python\nimport pandas\nA = pandas.read_csv('dir/file.csv')\n```\n\u00e2\u20ac\u2039\nThen execute ARCU using\n\n```python\nimport arcu\ncentroids = arcu.arcu(A,r,c,u)\n```\n\nwhere \n```\nInputs:\n A = dataframe of single cell location and feature data\n r = radius, in pixels, of regions in which to search for subpopulations\n c = the minimum number of cells a region of interest can contain to be considered for reporting\n u = the scaling coefficient on standard deviation for a cell to be considered interesting\n\nReturns:\n a dataframe containing the x,y coordinates of groupings that meet feature expression criteria\n```\n\n\n",
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