Multiomics: miRNA & RNA
Getting started
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:
Output:
all_samples.miRNA.UMIs.txtfile
Output:
<outputPrefix>.quant.genes.sffile
The data used below is from the report:
Kumar, S., Ramos, E., Hidalgo, A. et al. Integrated multi-omics analyses of synaptosomes revealed synapse-associated novel targets in Alzheimer’s disease. Mol Psychiatry 30, 5121–5136 (2025). https://doi.org/10.1038/s41380-025-03095-w
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
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
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.
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
This results in an additional 41 samples added to the study, totaling 82 samples.
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
The status will move from Pending to In progress to Complete.
Click the Refresh icon to see this update.
Click the Analysis name to open the analysis once complete.
There will be two starting nodes, miRNA and Quantification (mRNA) as shown below.
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.
This section will cover the analysis pipeline to generate the task graph shown below for mRNA data:
Add gene-level annotations to the quantified data.
Single-click the Quantification node.
Select Annotate features under the Pre-analysis tools section in the toolbox on the right.
Choose the genome and annotation files that match those used in DRAGEN then click Finish.
Normalize the data to prepare for downstream analysis.
Single-click the Annotated Counts node, then select the Normalization task from the Normalization and Scaling section.
Click the "Use Recommended" button or select an alternative method. We recommend the widely used Median ratio (DESeq2 only) method.
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.
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
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
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.
click Configure > Style and adjust the point size to 7
Click Plot > Export to save the visualization to the local machine
Identify enriched biological pathways or gene sets.
Left click the Filtered featured list node
Select Gene Set Enrichment from the Biological Interpretation section
Choose between KEGG Pathway Enrichment or Gene Set Ontology
Select the database
Click Finish
The latest version of KEGG can be added in the Settings > Library file management or by selecting New Library in the drop-down
Double click on the output Pathway enrichment node to open the report
Filter the report to less than 100 rows to enable View plots in Data viewer
Click on Gene set: path:hsa01040 (Biosynthesis of unsaturated fatty acids) to open this pathway
Hover over the parts of the pathway to view more details
This section will cover the analysis pipeline to generate the task graph shown below for miRNA data:
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
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.
Normalize the data to prepare for downstream analysis.
Single-click the Filtered counts node, then select the Normalization task from the Normalization and Scaling section.
Click the "Use Recommended" button or select an alternative method. We recommend the Median ratio (DESeq2 only) method.
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
Set up the comparison (AD vs HC) and leave other settings as default
Click Finish
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 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
The task generates a Targeted mRNA node, double click on it to open the 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
Identify enriched biological pathways or gene sets.
Click on Targeted mRNA node
Select Gene Set Enrichment from the Biological Interpretation section
Choose between KEGG Pathway Enrichment or Gene Set Ontology
Click Finish
Double click on the output Pathway enrichment node to open the report
Filter the report (e.g. Enrichment score > 5) to less than 100 rows
When the table has less than 100 rows, Data viewer plots are enabled. Click on the individual Gene set to see more details about the pathway.
Click View plots in Data Viewer to display the pathways in a bar chart and scatterplot
Combine mRNA and miRNA for analysis
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
Choose Merge features
Click Select data node
Left click the other Normalized counts node (mRNA)
Click Select
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.
Click Finish
Outcome:
Task node: Merge matrices
Result node: Merged counts
Visualize sample clustering and variance.
Select the Merged counts node
Choose Exploratory analysis > PCA from the toolbox
Keep the default PCA task settings
Click Finish
Double click the PCA result node to open the results in the Data viewer
The PCA task report includes a 3D scatterplot of the first 3 PCs, Scree plot, and Component loadings table:
Click on the analysis title in the breadcrumb to navigate back to the analyses pipeline.
Filter merged counts by features from both assays
There are different ways to achieve this goal:
Add a feature list including all of the features of interest then using the Filter features task > Saved list option
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
Click Setup > + 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
Click the control Configure > Axes
Using the drop-down to select the data (miRNA ID)
Click the data node to change the data node to the other Filtered feature list by selecting the appropriate node
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
Click Selection > Select and filter
Click Include selected points under Filter
Click Apply feature filter
Choose the Merged counts node and click Select
This produces a Filtered counts node on the task graph that we will use in the Hierarchical clustering / heatmap section.
Keep the selection of both and navigate to Plot > Additional actions
Click Create feature list
Name the list 'miRNA and mRNA'
Click Save
This will produce a feature list which we will use later in this walkthrough in the Correlation section.
Left click the Filtered counts node and click Exploratory analysis > Hierarchical clustering / heatmap in the task menu
Change the Sample order to Assign order and choose Type
Keep the rest of the default settings and click Finish
Double click to open the heatmap report in the Data viewer
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
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
Click Select data node
Choose the mRNA 'Features in miRNA and mRNA' node
Click Select
Keep the default settings the same
Click Finish
Double click the Correlation pair list task report to open the report
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.
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.
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