# Multiomics: miRNA & RNA ## Getting started ### [Logging into Connected Multiomics](https://help.multiomics.illumina.com/icm/introduction/readme-1#log-in-to-connected-multiomics) ### [Upload data to ICM](https://help.multiomics.illumina.com/icm/introduction/upload-data-files) ### [Study management](https://help.multiomics.illumina.com/icm/studies/create-study) ### [Analysis and running tasks](https://help.multiomics.illumina.com/icm/analyses/view-analyses-across-studies) ### 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](https://help.connected.illumina.com/icm/analyses/analysis-functionality/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: [DRAGEN miRNA](https://help.multiomics.illumina.com/dragen-mirna) * Output: `all_samples.miRNA.UMIs.txt` file [DRAGEN RNA](https://help.dragen.illumina.com/product-guide/dragen-v4.4/dragen-rna-pipeline/gene-expression-quantification) * Output: `.quant.genes.sf` file 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](https://rdcu.be/eQDIT). *Mol Psychiatry* 30, 5121–5136 (2025). [https://doi.org/10.1038/s41380-025-03095-w](https://www.nature.com/articles/s41380-025-03095-w) *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**

* 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. {% hint style="warning" %} 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](https://help.connected.illumina.com/icm/studies/view-studies/sample-metadata). {% endhint %}

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**

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)

{% hint style="warning" %} 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. {% endhint %} ## [Processing the mRNA data](https://help.connected.illumina.com/icm/analyses/walkthroughs/bulk-rna) This section will cover the analysis pipeline to generate the task graph shown below for mRNA data:

### [Annotate Features](https://help.connected.illumina.com/icm/analyses/analysis-functionality/task-menu/annotation-metadata/annotate-cells-1) 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**.

Use the Assembly and Annotation model that math those used in DRAGEN to annotate features

### [Normalization and Scaling](https://help.connected.illumina.com/icm/analyses/analysis-functionality/task-menu/normalization-and-scaling/normalization) 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.

Median ratio (DESeq2) is the recommended normalization method for mRNA data

### [Differential Analysis](https://help.connected.illumina.com/icm/analyses/analysis-functionality/task-menu/statistics/differential-analysis) 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](https://help.connected.illumina.com/icm/analyses/analysis-functionality/task-menu/statistics/differential-analysis/deseq2-r-vs-deseq2) and used the corresponding normalization prior.

Set up the differential analysis comparison

{% hint style="warning" %} 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](https://help.connected.illumina.com/icm/analyses/analysis-functionality/task-menu/filtering/filter-features) upstream in the analysis, often before normalization. {% endhint %} * 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

Filter the Differential analysis results then Generate filtered node and open the volcano plot

{% hint style="warning" %} 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. {% endhint %} The Volcano plot, box and whisker plots, and results table can be modified using the [data viewer](https://help.connected.illumina.com/icm/analyses/analysis-functionality/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

### [Gene Set enrichment](https://help.connected.illumina.com/icm/analyses/analysis-functionality/task-menu/biological-interpretation/gene-set-enrichment) Identify enriched biological pathways or gene sets. * Left click the Filtered featured list node * Select **Gene Set Enrichment** from the *Biological Interpretation* section

Gene set enrichment task can be used for KEGG or Gene set database analysis

* Choose between **KEGG Pathway Enrichment** or **Gene Set Ontology** * Select the database * Click **Finish**

{% hint style="warning" %} The latest version of KEGG can be added in the **Settings > Library file management** or by selecting **New Library** in the drop-down {% endhint %} * 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**

Filter the Pathway enrichment report to less than 100 rows to enable **View plots in Data viewer**

* Click on Gene set name to open the pathway * Hover over the parts of the pathway to view more details ## [Processing the miRNA data](https://app.gitbook.com/o/-MWUoaZPOpY9hR_8vqOU/s/5WPPw051cYE3Zthy5U7m/~/changes/90/analyses/walkthroughs/microrna) This section will cover the analysis pipeline to generate the task graph shown below for miRNA data:

### [Filter Low Expressing miRNA](https://help.connected.illumina.com/icm/analyses/analysis-functionality/task-menu/filtering/filter-features) Filtering is optional and subjective to the study. [Annotate features](https://help.connected.illumina.com/icm/analyses/analysis-functionality/task-menu/annotation-metadata/annotate-cells-1) 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

{% hint style="warning" %} 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. {% endhint %} ### [Normalization and Scaling](https://help.connected.illumina.com/icm/analyses/analysis-functionality/task-menu/normalization-and-scaling) 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.

Click Use recommended button to normalize with Median ratio (DESeq2 only) method

### [Differential Analysis](https://help.connected.illumina.com/icm/analyses/analysis-functionality/task-menu/statistics/differential-analysis) 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](https://help.connected.illumina.com/icm/analyses/analysis-functionality/task-menu/statistics/differential-analysis/deseq2-r-vs-deseq2) 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 the Differential analysis results then **Generate filtered node**

{% hint style="warning" %} Filter thresholds are at the discretion of the user for the study. {% endhint %} ### 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:

**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 ### [Gene Set Enrichment](https://help.connected.illumina.com/icm/analyses/analysis-functionality/task-menu/biological-interpretation/gene-set-enrichment) 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**

Use Gene set enrichment to identify enriched biological pathways or gene sets

* Double click on the output *Pathway enrichment* node to open the report

Double click the Pathway enrichment report in the miRNA task graph

* Filter the report (e.g. Enrichment score > 5) to less than 100 rows

{% hint style="warning" %} 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. {% endhint %} * Click **View plots in Data Viewer** to display the pathways in a bar chart and scatterplot

Filtered Pathway enrichment report in the Data viewer

## Combine mRNA and miRNA for analysis ### [Merge matrices](https://help.connected.illumina.com/icm/analyses/analysis-functionality/task-menu/pre-analysis-tools/merge-matrices) 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

{% hint style="warning" %} 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. {% endhint %} * Click **Finish**

Merge features within the miRNA and mRNA matrices

Outcome: * Task node: *Merge matrices* * Result node: *Merged counts* ### [Dimension reduction (PCA)](https://help.connected.illumina.com/icm/analyses/analysis-functionality/task-menu/exploratory-analysis/pca) Visualize sample clustering and variance. * Select the Merged counts node * Choose **Exploratory analysis > PCA** from the toolbox

Choose the PCA task from the Exploratory analysis section in the task menu

* Keep the default PCA task settings * Click **Finish**

* Double click the PCA result node to open the results in the Data viewer

Double click to open the PCA task result

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: 1. Add a [feature list](https://help.connected.illumina.com/icm/analyses/analysis-functionality/settings/lists) including all of the features of interest then using the [**Filter features task**](https://help.connected.illumina.com/icm/analyses/analysis-functionality/task-menu/filtering/filter-features) **> Saved list** option 2. Use the [Venn diagram](https://help.connected.illumina.com/icm/analyses/analysis-functionality/data-viewer/list-generator-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. 3. 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

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

* 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](#hierarchical-clustering-heatmap). * 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](https://help.connected.illumina.com/icm/analyses/analysis-functionality/settings/lists) which we will use later in this walkthrough in the [Correlation section](#correlation).

Create a feature list from the selection

### [Hierarchical clustering / heatmap](https://help.connected.illumina.com/icm/analyses/analysis-functionality/task-menu/exploratory-analysis/hierarchical-clustering) * 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**

The task can used to make a heatmap or bubble map by adjusting the settings

* Double click to open the heatmap report in the Data viewer

Use the plot settings on the left to configure the heatmap

### [Correlation](https://help.connected.illumina.com/icm/analyses/analysis-functionality/task-menu/statistics/correlation-analysis-1) 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.

## 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.