Metadata plugin for use in the OMERO CLI.
Project description
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 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.
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
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