pygpseq 3.4.1

A GPSeq image analysis package

!DISCLAIMER!

1. This package is not currently maintained. A new package that will include all pygpseq features is being implemented at radiantkit.
2. This package has been developed and tested ONLY for Python3.6, which will reach its end of life On December 23rd, 2021.
3. Versions 3.4.* of this package only change package dependencies to fix an issue due to incorrect dependency declaration.

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pyGPSeq

A Python3.6 package that provides tools to analyze images of GPSeq samples. Read the Wiki documentation for more details.

Requirements

Python3.6 and compatible tkinter package are required to run pygpseq. On Ubuntu 20.04, you can install them with:

sudo add-apt-repository ppa:deadsnakes/ppa
sudo apt install python3.6
sudo apt install python3.6-tk

Installation

We recommend installing pygpseq using poetry. Check how to install poetry here if you don't have it yet! Once you have poetry ready on your system, you can install the package in its own virtual environment with:

git clone https://github.com/ggirelli/pygpseq.git
cd pygpseq
poetry install

And then enter the environment with poetry shell.

Alternatively, if you prefer to use conda , you can setup an environment with:

conda create -n pygpseq python=3.6
conda activate pygpseq
conda install pip
conda install -c anaconda libtiff 

Usage

Analyze a GPSeq image dataset

The gpseq_anim (GPSeq analysis of images) analyzes a multi-condition GPSeq image dataset. Run gpseq_anim -h for more details.

Calculate lamin distance of FISH signals

The gpseq_fromfish script characterizes FISH signals identified with DOTTER (or similar tools) by calculating: absolute/normalized distance from lamina and central region, nuclear compartment, allele status,... Run gpseq_fromfish -h for more details.

Merge multiple FISH analyses using a metadata table

Use the gpseq_fromfish_merge script to merge multiple FISH analysis output (generated with gpseq_fromfish). For more details run gpseq_fromfish_merge -h.

Perform automatic 3D nuclei segmentation

Run tiff_auto3dseg -h for more details on how to produce binary/labeled (compressed) masks of your nuclei staining channels

Identify out of focus (OOF) fields of view

Run tiff_findoof -h for more details on how to quickly identify out of focus fields of view. Also, the tiff_plotoof script (in R, requires argparser and ggplot2) can be used to produce an informative plot with the signal location over the Z stack.

Split a tiff in smaller images

To split a large tiff to smaller square images of size N x N pixels, run tiff_split input_image output_folder N. Use the --enlarge option to avoid pixel loss. If the input image is a 3D stack, then the output images will be of N x N x N voxels, use the --2d to apply the split only to the first slice of the stack. For more details, run tiff_split -h.

(Un)compress a tiff

To uncompress a set of tiff, use the tiffcu -u command. To compress them use the tiffcu -c command instead. Use tiffcu -h for more details.

Convert a nd2 file into single-channel tiff images

Use the nd2_to_tiff tool to convert images bundled into a nd2 file into separate single-channel tiff images. Use nd2_to_tiff -h for the documentation.

Contributing

We welcome any contributions to pygpseq. Please, refer to the contribution guidelines if this is your first time contributing! Also, check out our code of conduct.

License

MIT License
Copyright (c) 2017-21 Gabriele Girelli