Skip to content

Getting started

Coppafisher

Coppafisher is an open source data analysis Python package for COmbinatorial Padlock-Probe-Amplified Fluorescence In Situ Hybridization (coppafish) datasets. A series of 3D microscope images are arranged into tiles, rounds and channels. For each sequencing round, every wanted gene spot is fluoresced by a dye. By the end of all rounds, each gene has a unique, barcode-like sequence of dyes called the gene code. Coppafisher is a data analysis pipeline to assign genes to spots by their gene codes in 3D.

See installation on how to install our software, and usage to run coppafisher on your dataset. For details about coppafisher's methodology, see the method. Some vocabulary might be unfamiliar, please see the glossary for reference.

Simple Schematic
Gene calling on a tile.

Installation

Prerequisites

  • Windows or Linux. MacOS is not tested.
  • Python 3.11 or 3.12.
  • Git.
  • 64GB of memory for tile sizes ~64x2048x2048 pixels (recommended).
  • Nvidia GPU with Cuda 12.4 support (optional).

Environment

Install coppafisher software from within an environment. We will use a conda environment, so miniconda or anaconda is required.

First, build an environment and update pip (recommended)

conda create -n coppa python=3.12
conda activate coppa
python -m pip install --upgrade pip

coppa can be changed to any name.

Environment naming

Avoid putting the word coppafisher into the environment name because this is the same name as the Python package, which could cause bugs.

Install

Clone the latest coppafisher version locally

git clone --depth 1 https://github.com/paulshuker/coppafisher.git
Install a specific version

You can instead install specific coppafisher versions, like version 1.0.0

git clone --depth 1 --branch 1.0.0 https://github.com/paulshuker/coppafisher.git

Check the tags for version options.

install package dependencies

cd coppafisher
python -m pip install -r requirements.txt

install PyTorch with both CPU and Cuda 12.4 support by

python -m pip install -r requirements-torch.txt
Check the GPU is detected

If you have an Nvidia GPU with working drivers, you can check that it is detected in the python terminal

import torch
torch.cuda.is_available()

which should show true.

Finally, install coppafisher by

python -m pip install .

You can now safely delete the locally cloned coppafisher repository

cd ..
rm -rf coppafisher

Updating

Coppafisher will not automatically install updates. But, you will see a warning at the start of a pipeline if a new online version is available.

To update version, delete the old conda environment by

conda env remove -yn coppa

Now follow all installation instructions again.

Keep all output data (including the notebook) when updating coppafisher versions. If data saved to disk is now deprecated, coppafisher will automatically suggest a course of action when it is run again.