Data / Task nodes and Performing tasks in Connected Multiomics
Within a study, the Analyses tab contains two elements: task nodes (rectangles) and data nodes (circles) connected by lines and arrows. Collectively, they represent a data analysis pipeline.
Clicking a data node brings up a context sensitive menu on the right. This menu changes depending on the type of data node. It will only present tasks which can be performed on that specific data type. Hover over the task to obtain additional information regarding each option.
Select the task you wish to perform from the menu. When configuring task options, additional information regarding each option is available. Click Finish to perform the task.
Depending on the task, a new data node may automatically be created and connected to the original data node. This contains the data resulting from the task. Tasks that do not produce new data types will not produce an additional data node.
To view the results of a task, click the data node and choose the Task report option on the menu.
Viewing and saving data
All data contained in data nodes can be downloaded to the local machine by selecting the node and navigating to the bottom of the toolbox then choose Download data.
The Data Viewer can be used to plot, modify, and save data. In this walkthrough the PCA data node and Hierarchical clustering / heatmap node can be automatically opened in the Data viewer by double-clicking the data node or opening the Task report from the toolbox.
To save an individual image within the Data Viewer to your machine, click Plot then Export image & select the format, size, and resolution then click Save. Use the plot-specific tools for this.
All visualizations within a sheet in the Data Viewer can be exported as one image (e.g. use one image with all plots for a poster). Use the Export drop-down at the top of the data-viewer for this and select Export image.
Input: secondary outputs from the DRAGEN analysis
The DRAGEN miRNA & DRAGEN RNA pipelines can be used to generate the file types:
NOTE: This walkthrough demonstrates how to ingest two omic types within a single pipeline. While we illustrate the process using miRNA + RNA, the same approach can be extended to other assay types.
Create new study
Create a study to upload data.
Click + New Study
Create a new study
Add Study Name and Description
Click Create
Click + Add Data
Add data to the study
Choose Select from ICA project
Choose Bulk > miRNA
Click the + Add Demo Data button
Select the 'Multiomics-Demo-Data' folder
Select the 'Transcriptomics' folder
Select the 'miRNA + RNA Kumar et al 2025 Demo data' folder
Check the 'MetaData.tsv' and 'all_kumar_samples.miRNA.UMIs.txt' files
Click Add selected data to your study
Add the miRNA data to the study
This will result in 41 samples added to the study and the metadata.
In this case, the MetaData.tsv file includes the metadata for the 41 samples. The process above illustrates adding a metadata file from ICA. This process is different from adding a metadata file from the local machine.
The miRNA data is added to the study as samples
Click + Add Data
Choose Select from ICA project
Choose Bulk > RNA-Seq
Choose Illumina DRAGEN RNA
Click Select format
Select the 'Multiomics-Demo-Data' folder
Select the 'Transcriptomics' folder
Select the 'miRNA + RNA Kumar et al 2025 Demo data' folder
Select the 'salmon_sf' folder
Check all the files using the checkbox left of Name
Click Add selected data to your study
Add mRNA data to the study
This results in an additional 41 samples added to the study, totaling 82 samples.
The mRNA data is added to the study resulting in 82 total samples (41 mRNA and 41 miRNA)
Create new analysis
After uploading data to ICM, create a new analysis.
Click the +New Analysis button in top right of the study
Enter the Analysis name, choose Analysis Type as Custom: Multiomics, and choose the sample groups to include
Click Run Analysis
Choose 'Custom: Multiomics' as the Analysis Type
The status will move from Pending to In progress to Complete.
Click the Refresh icon to see this update.
Click the Refresh button to see analysis updates
Click the Analysis name to open the analysis once complete.
There will be two starting nodes, miRNA and Quantification (mRNA) as shown below.
starting nodes include miRNA and Quantification (mRNA)
There are 82 samples, 41 miRNA and 41 mRNA as indicated by hovering on the two respective nodes. The Metadata tab will show 41 samples, indicating that the miRNA and mRNA has been integrated at the attribute level.
Compare gene expression across experimental groups.
From the Normalized counts node, select Differential Analysis from the Statistics section.
Choose your preferred model and set up the comparison. Note that we have chosen the DESeq2 method and used the corresponding normalization prior.
Set up the differential analysis comparison
If you have not chosen to filter features upstream in the analysis, the Low value filter will default to filter using the geometric mean. An alternative is to filter features upstream in the analysis, often before normalization.
Double-click or single click and open the task report from the toolbox to view the results
Open the differential analysis results
Filter the task report using the toolbox on the left
Once happy with the filtering, click the Generate filtered node button at the bottom of the toolbox
Click the volcano plot icon next the comparison header to view the plot in the data viewer
Filter the Differential analysis results then Generate filtered node and open the volcano plot
Note all of the icons available in the View column for each of the genes, including dot plots. Additional column information can be added using the Optional columns button at the top right of the table. Columns can be sorted by clicking the headers. Download is available at the top left of the table. Scroll to the bottom of the table to show an alternate number of rows and page results.
The Volcano plot, box and whisker plots, and results table can be modified using the data viewer controls.
Open the volcano plot in the Data viewer
click Configure >Style and adjust the point size to 7
Use Configure icon options to optimize visualization settings
Click Plot > Export to save the visualization to the local machine
Click Plot then Export to save the visualization to the machine
Filtering is optional and subjective to the study. Annotate features is also optional and can be used to get genomic location information of the miRNA by linking the data to the miRBase annotation; the genome and annotation files should match those used in DRAGEN.
Click on the miRNA data node
In the toolbox select Filtering > Filter features
Select Noise reduction filter type (default) and exclude features where max is <=10
Click Finish
Filter out miRNA that have low expression
Mouse over the output Filtered counts data node to check how many features in the filtered data node. If the number is too low, you might want to redo the filter and use a more lenient filter criteria.
Compare miRNA expression across experimental groups.
From the Normalized counts node, select Differential Analysis from the Statistics section.
Choose your preferred model and set up the comparison. Note that we have chosen the DESeq2 method and used the corresponding normalization prior.
Select Type > Add factors
Click Next
Select factors for analysis
Set up the comparison (AD vs HC) and leave other settings as default
Click Finish
Set up comparisons
The DESeq2 report is generated on the task graph, double click on the result node to open the report:
Use the toolbox filters to filter the list of miRNAs
Click Generate Filtered node to create a new node containing only the filtered miRNAs on the task graph
Filter the Differential analysis results then Generate filtered node
Filter thresholds are at the discretion of the user for the study.
miRNA integration
Generate the targeted mRNA list
Click on the filtered feature list
Select miRNA integration > Get targeted mRNA in the task menu
Select the database
Click Finish
Get targeted mRNAs from the list of miRNAs
The task generates a Targeted mRNA node, double click on it to open the report:
Targeting miRNA report
Context++ score: estimating the strength of repression, more negative values suggest stronger predicted repression, this is the key metric for ranking predicted targets
Context++ score percentile: percentile ranking of the score compared to all predictions, higher percentile means stronger predicted effect
Weighted Context++ score: adjusted score considering multiple sites in the same transcript
Predicted relative KD: predicted relative dissociation constant is to measure the affinity between miRNA and targeted mRNA, lower KD means stronger binding affinity, higher KD means weaker binding
Different data matrices can be merged in order to achieve the analysis goals. In this case, two assays (miRNA expression and mRNA expression) were performed on the same populations so the expression matrices can be merged for joint analysis.
Select the Normalized counts node
Choose the Pre-analysis tools > Merge matrices task
Use the Merge matrices task to merge miRNA & mRNA features
Choose Merge features
Click Select data node
Left click the other Normalized counts node (mRNA)
Click Select
Use Merge features and select the node to merge with in the task
The data nodes that can be merged are shown in color on the task graph, other data nodes are disabled (greyed out). Left click on the data node that you want to merge with the current one and click the Select button.
Use the Venn diagram on the bottom of the analyses tab. Briefly, select the nodes of interest and display the selection. As selections are made, the Current selection on the right will change. At the bottom of the page, check Select all to include both lists in the Current selection then at the bottom of Current selection, click Filter features by list and choose the Merged Counts node.
Use the Data viewer Venn Diagram to create a filtered node on the pipeline which is covered below:
Click the Data viewer tab
Click Start a new session
Start a new Data viewer session
Click Setup > + New plot
Add a New plot
Choose Venn diagram as the plot type
Select data by typing Filtered feature list and select one of the Filtered feature list nodes
Choose the data node to plot
Click the control Configure > Axes
Using the drop-down to select the data (miRNA ID)
Add data to the plot
Click the data node to change the data node to the other Filtered feature list by selecting the appropriate node
Add additional data to the plot
Add the data from the other node (Gene name)
Press and hold Ctrl or Shift and click to select the red and green circle together
Press and hold Ctrl or Shift and click to make selections
Click Selection > Select and filter
Click Include selected points under Filter
Click Apply feature filter
Filter the selection and Apply the feature filter to the pipeline
Correlation across assays can be used to analyze every feature in one assay vs every feature in the other assay. It is recommended to first filter the two count matrix data nodes to only include features of interest to reduce computation.
Left click the miRNA Normalized counts node
Select the Filtering > Filter features task
Choose Filter type as Saved list
Select the list as the previously saved list called 'miRNA and mRNA' from 'Filter merged counts by features from both assays' section in this walkthrough
Click Finish
Filter features using the previously saved list
Repeat this process on the mRNA Normalized counts node
This will produce two Filtered counts node containing the filtered features
Left click the miRNA 'Features in miRNA and mRNA' node
Click Statistics > Correlation to run the Correlation task from the toolbox
Choose Correlation across assays
Click Next
Choose Correlation across assays as the method to choose for correlation analysis
Click Select data node
Choose the mRNA 'Features in miRNA and mRNA' node
Click Select
Select the data node to be compared to the node the task has been invoked from
Keep the default settings the same
Click Finish
Correlation across assays task with default settings
Double click the Correlation pair list task report to open the report
Double click the Correlation pair list report to see the results
The report can be downloaded to your machine by clicking Download.
Additional columns can be shown by clicking Optional columns.
Columns can be sorted by clicking the arrows in the column header and typing in the column text box.
View correlation plots in the Data viewer by clicking the icon for each row in the table.
Correlation report
Complete task graph
The task graph below shows the completed analysis. Orange indicates miRNA specific analysis. Blue indicates mRNA specific analysis. Green indicates analysis using both miRNA and mRNA.
