= Create OMERO.tables from a csv file =
==**Description:**==
**OMERO.tables** unifies the storage of columnar data from various sources, such as automated analysis results or script-based processing, and makes them dynamically available within OMERO.
==**Ressources:**==
**Dataset**: Copy the folder called {nav NEDD} from:
WIN: \\svraw1.epfl.ch\ptbiop\public\1-Training\OMERO\demo dataset analysis v2
MAC: smb://svraw1.epfl.ch/ptbiop/public/1-Training/OMERO/demo dataset analysis v2
**CSV file**: Copy the file //four-NEDD-images.csv// from:
WIN: \\svraw1.epfl.ch\ptbiop\public\1-Training\OMERO\demo extra files
MAC: smb://svraw1.epfl.ch/ptbiop/public/1-Training/OMERO/demo extra files
==**Step-by-Step:**==
# Upload the folder called {nav NEDD} to OMERO **as a Dataset** and in OMERO, name this dataset NEDD.
# In OMERO.web, attach the //four-NEDD-images.csv// file to your NEDD Dataset using the {nav +} button under the {nav Attachments} harmonica from the right panel. {F20393539, width=300, style=inline}
# Select the NEDD dataset and click on the {icon cogs} button** from the top menu bar of omero web **.
# Go to {nav import_scripts > Populate Metadata...}. Run the script
# Select the NEDD dataset from the left-hand pane and observe in the right hand pane under “Attachments” that there is a new file called //bulk_annotations// attached to your NEDD dataset. This is your newly created OMERO.table, that can be opened from the web by hitting the {icon eye} button. It contains in each line a dynamic link to the corresponding image in OMERO.web {F20393719, width=500}
Back to OMERO.web's main page, clic on one of the images from the {nav NEDD} dataset without opening it with the iviewer. Look at the {nav Tables} harmonica from the right-hand pane. Observe that all the statistics from the OMERO.table that apply to the selected image are gathered here. {F20393728, width=300}
= 2. Create OMERO.tables from FIJI's scripting editor =
Follow the //Analyze OMERO data in batch using the Scripting editor in Fiji// step-by-step tutorial from [[ https://c4science.ch/w/bioimaging_and_optics_platform_biop/image-processing/omero/analysis/fiji/ | this page]]
= 3. Analyze metadata using OMERO.parade =
==**Description:**==
**OMERO.parade** is a metadata-mining plugin for OMERO.web. It enables access to the metadata of images in OMERO for plotting, display and filtering of images. Supported metadata includes number of ROIs, Key-Value pairs, and data stored in OMERO.tables or in .csv files. OMERO.tables are not required to use the OMERO.parade data mining tool.
==**Ressources:**==
**Data**: Copy and import to OMERO the folder called {nav EdU RFP} from:
WIN: \\svraw1.epfl.ch\ptbiop\public\1-Training\OMERO\demo dataset analysis v2
MAC: smb://svraw1.epfl.ch/ptbiop/public/1-Training/OMERO/demo dataset analysis v2
**Scripts**:
Groovy script for running a macro in batch (on a whole OMERO dataset) using Fiji:
{F20243399}
Example analysis macro file (segmentation + positive cell counts in 2 channels):
{F20243419}
**Other requirements**:
Follow the //Analyze OMERO data in batch using the Scripting editor in Fiji// step-by-step tutorial from [[ https://c4science.ch/w/bioimaging_and_optics_platform_biop/image-processing/omero/analysis/fiji/ | this page]]
==**Step-by-Step:**==
**Filter images based on analysis data (from a .csv file or an OMERO.table)**
# Select the {nav Edu RFP} dataset, and drag and drop it in the hierarchy so that it is **not included in a project**.
# Choose the {nav Parade} option in the centre panel dropdown menu from OMERO.web's main page.
# In the {nav Add filter} selection box, select //my_csv_filename.csv//
# Select data from the result table using the drop-down menu (i.e PosCellCount3) and drag the slider to filter the Images. Add as many filters as you want to fine-tune the research. {F20393895, size=full, layout=inline}
**Create a dynamic scatter plot of your dataset based on analysis data (from a .csv file or an OMERO.table)**
# Remove all filters by hitting {nav X} and change the OMERO.parade layout to //Table// (middle button located under the {nav Parade} dropdown menu):
{F20393962, width=100}
# In the selection box {nav Add table data…}, select : {nav Table_%Area}, {nav ROI_count}, {nav Table_PosCellCount2}, {nav Table_PosCellCount3}. Note: you could also use the same data from the csv file.
# Note that it is currently not possible to remove a column.
# Click on the name of a column to sort it
# Check the checkbox in each column to show the Heatmap. Note the corresponding pattern in the Heatmap.
# Switch to the //Plot// layout (third button located under the {nav Parade} dropdown menu):
{F20393995, width=100}
# It takes the first 2 columns of pre-selected table data (from step 2.) to create a scatter plot. Each point corresponds to an image from the dataset.
# Filters can be added to plot only the relevant results. (See part //**Filter images based on analysis data (from a .csv file or an OMERO.table)**//)
# Try to dynamically change the axes: select Table_PosCellCount2 and Table_PosCellCount3
# Click on a point to select an outlier, or draw a rectangle by dragging your mouse to select several outliers at the same time
# Draw a rectangle over the points at (0.0) : all the double-negative (EdU-, RFP-) images are selected in the left-hande pane
{F20394032, size=full}
# Selected images can be annotated using the options from the right-hand pane, viewed with the iviewer, or even opened in OMERO.figures!
# Closing a Dataset in the left-hand tree removes the values from the plot.
WIN: \\svraw1.epfl.ch\ptbiop\public\1-Training\OMERO\demo dataset analysis v2
MAC: smb://svraw1.epfl.ch/ptbiop/public/1-Training/OMERO/demo dataset analysis v2
**CSV file**: Copy the file //four-NEDD-images.csv// from:
WIN: \\svraw1.epfl.ch\ptbiop\public\1-Training\OMERO\demo extra files
MAC: smb://svraw1.epfl.ch/ptbiop/public/1-Training/OMERO/demo extra files
==**Step-by-Step:**==
# Upload the folder called {nav NEDD} to OMERO **as a Dataset** and in OMERO, name this dataset NEDD.
# In OMERO.web, attach the //four-NEDD-images.csv// file to your NEDD Dataset using the {nav +} button under the {nav Attachments} harmonica from the right panel. {F20393539, width=300, style=inline}
# Select the NEDD dataset and click on the {icon cogs} button** from the top menu bar of omero web **.
# Go to {nav import_scripts > Populate Metadata...}. Run the script
# Select the NEDD dataset from the left-hand pane and observe in the right hand pane under “Attachments” that there is a new file called //bulk_annotations// attached to your NEDD dataset. This is your newly created OMERO.table, that can be opened from the web by hitting the {icon eye} button. It contains in each line a dynamic link to the corresponding image in OMERO.web {F20393719, width=500}
Back to OMERO.web's main page, clic on one of the images from the {nav NEDD} dataset without opening it with the iviewer. Look at the {nav Tables} harmonica from the right-hand pane. Observe that all the statistics from the OMERO.table that apply to the selected image are gathered here. {F20393728, width=300}
= 2. Create OMERO.tables from FIJI's scripting editor =
Follow the //Analyze OMERO data in batch using the Scripting editor in Fiji// step-by-step tutorial from [[ https://c4science.ch/w/bioimaging_and_optics_platform_biop/image-processing/omero/analysis/fiji/ | this page]]
= 3. Analyze metadata using OMERO.parade =
==**Description:**==
**OMERO.parade** is a metadata-mining plugin for OMERO.web. It enables access to the metadata of images in OMERO for plotting, display and filtering of images. Supported metadata includes number of ROIs, Key-Value pairs, and data stored in OMERO.tables or in .csv files. OMERO.tables are not required to use the OMERO.parade data mining tool.
==**Ressources:**==
**Data**: Copy and import to OMERO the folder called {nav EdU RFP} from:
WIN: \\svraw1.epfl.ch\ptbiop\public\1-Training\OMERO\demo dataset analysis v2
MAC: smb://svraw1.epfl.ch/ptbiop/public/1-Training/OMERO/demo dataset analysis v2
**Scripts**:
Groovy script for running a macro in batch (on a whole OMERO dataset) using Fiji:
{F20243399}
Example analysis macro file (segmentation + positive cell counts in 2 channels):
{F20243419}
**Other requirements**:
Follow the //Analyze OMERO data in batch using the Scripting editor in Fiji// step-by-step tutorial from [[ https://c4science.ch/w/bioimaging_and_optics_platform_biop/image-processing/omero/analysis/fiji/ | this page]]
==**Step-by-Step:**==
**Filter images based on analysis data (from a .csv file or an OMERO.table)**
# Select the {nav Edu RFP} dataset, and drag and drop it in the hierarchy so that it is **not included in a project**.
# Choose the {nav Parade} option in the centre panel dropdown menu from OMERO.web's main page.
# In the {nav Add filter} selection box, select //my_csv_filename.csv//
# Select data from the result table using the drop-down menu (i.e PosCellCount3) and drag the slider to filter the Images. Add as many filters as you want to fine-tune the research. {F20393895, size=full, layout=inline}
**Create a dynamic scatter plot of your dataset based on analysis data (from a .csv file or an OMERO.table)**
# Remove all filters by hitting {nav X} and change the OMERO.parade layout to //Table// (middle button located under the {nav Parade} dropdown menu):
{F20393962, width=100}
# In the selection box {nav Add table data…}, select : {nav Table_%Area}, {nav ROI_count}, {nav Table_PosCellCount2}, {nav Table_PosCellCount3}. Note: you could also use the same data from the csv file.
# Note that it is currently not possible to remove a column.
# Click on the name of a column to sort it
# Check the checkbox in each column to show the Heatmap. Note the corresponding pattern in the Heatmap.
# Switch to the //Plot// layout (third button located under the {nav Parade} dropdown menu):
{F20393995, width=100}
# It takes the first 2 columns of pre-selected table data (from step 2.) to create a scatter plot. Each point corresponds to an image from the dataset.
# Filters can be added to plot only the relevant results. (See part //**Filter images based on analysis data (from a .csv file or an OMERO.table)**//)
# Try to dynamically change the axes: select Table_PosCellCount2 and Table_PosCellCount3
# Click on a point to select an outlier, or draw a rectangle by dragging your mouse to select several outliers at the same time
# Draw a rectangle over the points at (0.0) : all the double-negative (EdU-, RFP-) images are selected in the left-hande pane
{F20394032, size=full}
# Selected images can be annotated using the options from the right-hand pane, viewed with the iviewer, or even opened in OMERO.figures!
# Closing a Dataset in the left-hand tree removes the values from the plot.