omero-metadata 0.8.1

Metadata plugin for use in the OMERO CLI.

OMERO metadata plugin

Plugin for use in the OMERO CLI. Provides tools for bulk management of annotations on objects in OMERO.

Requirements

• OMERO 5.6.0 or newer

• Python 3.6 or newer

Installing from PyPI

This section assumes that an OMERO.py is already installed.

Install the command-line tool using pip:

$ pip install -U omero-metadata

Note the original version of this code is still available as deprecated code in version 5.4.x of OMERO.py. When using the CLI metadata plugin, the OMERO_DEV_PLUGINS environment variable should not be set to prevent conflicts when importing the Python module.

Usage

The plugin is called from the command-line using the omero command:

$ omero metadata <subcommand>

Help for each command can be shown using the -h flag. Objects can be specified as arguments in the format Class:ID, such as Project:123.

Bulk-annotations are HDF-based tables with the NSBULKANNOTATION namespace, sometimes referred to as OMERO.tables.

Available subcommands are:

• allanns: Provide a list of all annotations linked to the given object

• bulkanns: Provide a list of the NSBULKANNOTATION tables linked to the given object

• mapanns: Provide a list of all MapAnnotations linked to the given object

• measures: Provide a list of the NSMEASUREMENT tables linked to the given object

• original: Print the original metadata in ini format

• pixelsize: Set physical pixel size

• populate: Add metadata (bulk-annotations) to an object (see below)

• rois: Manage ROIs

• summary: Provide a general summary of available metadata

• testtables: Tests whether tables can be created and initialized

populate

This command creates an OMERO.table (bulk annotation) from a CSV file and links the table as a File Annotation to a parent container such as Screen, Plate, Project or Dataset. It also attempts to convert Image or Well names from the CSV into Image or Well IDs in the OMERO.table.

The CSV file must be provided as local file with --file path/to/file.csv.

If you wish to ensure that number columns are created for numerical data, this will allow you to make numerical queries on the table. Column Types are:

• d: DoubleColumn, for floating point numbers

• l: LongColumn, for integer numbers

• s: StringColumn, for text

• b: BoolColumn, for true/false

• plate, well, image, dataset, roi to specify objects

These can be specified in the first row of a CSV with a # header tag (see examples below). The # header row is optional. Default column type is String.

NB: Column names should not contain spaces if you want to be able to query by these columns.

Examples:

To add a table to a Project, the CSV file needs to specify Dataset Name and Image Name:

$ omero metadata populate Project:1 --file path/to/project.csv

project.csv:

# header s,s,d,l,s
Image Name,Dataset Name,ROI_Area,Channel_Index,Channel_Name
img-01.png,dataset01,0.0469,1,DAPI
img-02.png,dataset01,0.142,2,GFP
img-03.png,dataset01,0.093,3,TRITC
img-04.png,dataset01,0.429,4,Cy5

This will create an OMERO.table linked to the Project like this:

Image Name	Dataset Name	ROI_Area	Channel_Index	Channel_Name	Image
img-01.png	dataset01	0.0469	1	DAPI	36638
img-02.png	dataset01	0.142	2	GFP	36639
img-03.png	dataset01	0.093	3	TRITC	36640
img-04.png	dataset01	0.429	4	Cy5	36641

If the target is a Dataset instead of a Project, the Dataset Name column is not needed.

To add a table to a Screen, the CSV file needs to specify Plate name and Well. If a # header is specified, column types must be well and plate.

screen.csv:

# header well,plate,s,d,l,d
Well,Plate,Drug,Concentration,Cell_Count,Percent_Mitotic
A1,plate01,DMSO,10.1,10,25.4
A2,plate01,DMSO,0.1,1000,2.54
A3,plate01,DMSO,5.5,550,4
B1,plate01,DrugX,12.3,50,44.43

This will create an OMERO.table linked to the Screen, with the Well Name and Plate Name columns added and the Well and Plate columns used for IDs:

Well	Plate	Drug	Concentration	Cell_Count	Percent_Mitotic	Well Name	Plate Name
9154	3855	DMSO	10.1	10	25.4	a1	plate01
9155	3855	DMSO	0.1	1000	2.54	a2	plate01
9156	3855	DMSO	5.5	550	4.0	a3	plate01
9157	3855	DrugX	12.3	50	44.43	b1	plate01

If the target is a Plate instead of a Screen, the Plate column is not needed.

If the target is an Image, a csv with ROI-level and object-level data can be used to create an OMERO.table (bulk annotation) as a File Annotation on an Image. The ROI identifying column can be an roi type column containing ROI ID, and Roi Name column will be appended automatically (see example below). Alternatively, the input column can be Roi Name (with type s), and an roi type column will be appended containing ROI IDs. In this case, it is required that ROIs on the Image in OMERO have the Name attribute set.

image.csv:

# header roi,l,d,l
Roi,object,probability,area
501,1,0.8,250
502,1,0.9,500
503,1,0.2,25
503,2,0.8,400
503,3,0.5,200

This will create an OMERO.table linked to the Image like this:

Roi	object	probability	area	Roi Name
501	1	0.8	250	Sample1
502	1	0.9	500	Sample2
503	1	0.2	25	Sample3
503	2	0.8	400	Sample3
503	3	0.5	200	Sample3

Note that the ROI-level OMERO.table is not visible in the OMERO.web UI right-hand panel, but can be visualized by clicking the “eye” on the bulk annotation attachment on the Image.

Developer install

This plugin can be installed from the source code with:

$ cd omero-metadata
$ pip install .

License

This project, similar to many Open Microscopy Environment (OME) projects, is licensed under the terms of the GNU General Public License (GPL) v2 or later.

Copyright

2018-2020, The Open Microscopy Environment