Prerequisites

Create a Python virtual environment and install the prerequisites from the requirements file:

virtualenv /tmp/venv
/tmp/venv/bin/pip install -r requirements.txt

Download OMERO.py from the OMERO downloads and extract it.

Activate the virtual environment, and check the metadata command is registered by the command-line interface shipped with OMERO.py:

/tmp/venv/bin/activate
cd /path/to/OMERO.py
bin/omero metadata -h

Annotating images

The image annotation is done in three steps:

• first an CSV file needs to be created containing the annotations.
• then the CSV file is converted into an OMERO.table attached to the top-level Project.
• finally the OMERO.table can be converted into a series of map annotations for each image.

Annotation file

This file can have as many columns as desired. For images in a Project/Dataset/Hierarchy, each row correspond to an image and two columns Dataset Name and Image Name are required to find the image.

Bulk annotations

First create an OMERO.table (bulk_annotations) attached the top-level project from the annotation.csv file:

bin/omero metadata populate --file annotation.csv Project:<id>

With OMERO 5.4.8, each row of this table should be viewable in the Web client by expanding the Tables tab on the right-hand panel of each image.

Map annotations

This conversion is done by a configuration file which describes which columns should be turned into key/value pairs and whether some columns should be grouped together using a namespace e.g. Organism.

Run the metadata populate command with the bulkmap context:

bin/omero metadata populate --context bulkmap --cfg bulkmap.yml Project:<id>

This should create map annotations for each image that can be viewed in the right-hand panel of the web client. Some of these annotations will have namespaces allowing them to be searchable using the OMERO.mapr web app.