This KNIME workflow is designed to facilitate the loading of image data from OMERO. It includes key preprocessing steps for VAST data, such as metadata creation and the linking of Key-Value Pairs.

• Fetching Images: The first step involves fetching images from a locally accessible folder.
• User Authentication: Users are prompted to input their OMERO username and password through a Java snippet. This information is then converted into variables that can be used by the Python script node. ...

Workflow to perform nuclei cell counting on High Content Screening (HCS) Data and upload result into OMERO

In this workflow, cell images are first uploaded to both Galaxy and OMERO using the “OMERO Image Import” tool. Concurrently, image processing is performed. After thresholding and binarization, key features of nuclei, such as area, label number, and perimeter, are computed from the processed images and saved as a CSV file. The result file is then attached to each image stored in OMERO using ...

General workflow to upload data into OMERO using Galaxy

A dataset for testing can be found at: https://zenodo.org/records/14205500

Important Security Note: It is crucial to be aware that storing credentials as variables can pose security risks, particularly if accessed by administrators. Therefore, it is essential to handle user credentials securely and in accordance with best practices.

General workflow to upload data into OMERO using KNIME

The workflow consists of two main branches: the Green Branch, which imports a folder containing images, and the Purple Branch, which enables the annotation of metadata as key-value pairs.

• Fetching Images: The first step involves fetching images from a locally accessible folder.
• User Authentication: Users are prompted to input their OMERO username and password through a Java snippet. This information is then converted into ...

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MMV Im2Im Transformation

A generic python package for deep learning based image-to-image transformation in biomedical applications

The main branch will be further developed in order to be able to use the latest state of the art techniques and methods in the future. To reproduce the results of our manuscript, we refer to the branch ...

This workflow has been created as part of Demonstrator 6 of the project EOSC-Life (within WP3) and is focused on reusing publicly available RNAi screens to gain insights into the nucleolus biology. The workflow downloads images from the Image Data Resource (IDR), performs object segmentation (of nuclei and nucleoli) and feature extraction of the images and objects identified.

Tutorial: https://training.galaxyproject.org/training-material/topics/imaging/tutorials/tutorial-CP/tutorial.html

IDR is based on OMERO and thus all what we show in this notebook can be easily adjusted for use against another OMERO server, e.g. your institutional OMERO server instance.

The main objective of this notebook is to demonstrate how public resources such as the IDR can be used to train your neural network or validate software tools.

The authors of the PLOS Biology paper, "Nessys: A new set of tools for the automated detection of nuclei within intact tissues and dense 3D cultures" published in August ...

Learning objectives

• Read data to analyse from an object store.
• Analyse data in parallel using Dask.
• Show how to use public resources to train neural network.
• Load labels associated to the original data
• Compare results with ground truth.

The authors of the PLOS Biology paper, "Nessys: A new set of tools for the automated detection of nuclei within intact tissues and dense 3D cultures" published in August 2019: https://doi.org/10.1371/journal.pbio.3000388, considered several image ...

The image is referenced in the paper "NesSys: a novel method for accurate nuclear segmentation in 3D" published August 2019 in PLOS Biology: https://doi.org/10.1371/journal.pbio.3000388 and can be viewed online in the Image Data Resource.

This original image was converted into the Zarr format. The analysis results produced by the authors of the paper were converted into labels and linked to the Zarr file which was placed into a public ...

Learning Objectives

• How to access genomic resource via its Python API
• How to access image resource via its Python API
• Relate image data to genomic data

Diabetes related genes expressed in pancreas

This notebook looks at the question Which diabetes related genes are expressed in the pancreas? Tissue and disease can be modified.

Steps:

• Query humanmine.org, an integrated database of Homo sapiens genomic data using the intermine API to find the ...

BatchConvert

A command line tool for converting image data into either of the standard file formats OME-TIFF or OME-Zarr.

The tool wraps the dedicated file converters bfconvert and bioformats2raw to convert into OME-TIFF or OME-Zarr, respectively. The workflow management system NextFlow is used to perform conversion in parallel for batches of images.

The tool also wraps s3 and Aspera clients (go-mc and aspera-cli, respectively). ...

Workflow for tracking objects in Cell Profiler: https://training.galaxyproject.org/training-material/topics/imaging/tutorials/object-tracking-using-cell-profiler/tutorial.html