Radiantkit

Radiantkit is a Python package containing tools for full-stack image analysis YFISH images.

This repository is a fork of the archived ggirelli/radiantkit with the aim to keep the code up to date with current Python versions.

The CHANGELOG will describe any changes to the original repository.

If you want to get in touch, please open an issue ticket.

Image: Adapted from Fig.1 GPSeq reveals the radial organization of chromatin in the cell nucleus.

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Installation instructions

For full and detailed installation instructions and usage read the full documentation here.

For use with SLURM + conda env

1. Clone the repository:

   git clone https://github.com/BiCroLab/radiantkit.git

2. Create a conda env from yaml file provided (radiant-kit-env.yml):

   conda env create -f radiant-kit-env.yml

3. Ensure your images are saved all together in .nd2 format in a single directory.

4. Modify the radiantK_SLURM_jobscript.sh to include correct parameters for your imaging data.

5. Submit the job to SLURM:

   sbatch radiantK_SLURM_jobscript.sh

Frequently Asked Questions (FAQ)

Q. What file formats does this pipeline accept?

We currently support:

• .nd2: first step is to convert to .tif (see radiantK_SLURM_jobscript.sh).
• .tif / .tiff: recommended (channel-separated)

Q. What kind of fluorescent imaging techniques is this software compatible with?

• Confocal
• Spinning disk confocal
• Widefield (if first deconvolved e.g. using deconwolf)

Q. Should I use 2D or 3D segmentation?

This depends on the application and cell-type. In general we would recommend using 2D segmentation if working with nuclei that are not round/uniformly shaped.