ISS Patcher

iss_patcher is a simple package for approximating features not experimentally captured in low-dimensional data based on related, high-dimensional data. The shared feature space between the two objects is identified, and log-normalised and z-scored on a per-object basis. The nearest neighbours of the low-dimensional observations in the high-dimensional space are identified, and the counts of the absent features are approximated as the mean of the high-dimensional neighbours.

While the function was initially written for processing ISS and GEX data, it can in principle be used for any sort of low-dimensional data featuring a subset of features from high-dimensional data.

System requirements

show requirements

Hardware requirements

iss_patcher can run on a standard computer with enough RAM to hold the used datasets in memory.

Software requirements

OS requirements

The package has been tested on:

• macOS Monterey (12.6.7)
• Linux: Ubuntu 18.04.6 bionic

Python requirements

A python version >=3.7 and <3.12 is required for all dependencies to work. Various python libraries are used, listed in pyproject.toml, including the python scientific stack with scipy>=1.6.0, annoy and scanpy. iss_patcher and all dependencies can be installed via pip (see below).

Installation

Optional: create and activate a new conda environment (with python<3.12):

mamba create -n iss_patcher "python<3.12"
mamba activate iss_patcher

from github

pip install git+https://github.com/Teichlab/iss_patcher.git

(installation time: around 2 min)

Usage and Documentation

Please refer to the demo notebook. Docstrings detailing the arguments of the various functions can be accessed at ReadTheDocs.

(demo running time: around 10 min)

Citation

iss_patcher is part of the forthcoming manuscript "A multiomic atlas of human early skeletal development" by To, Fei, Pett et al. Stay tuned for details!