Illumina Connected Multiomics Walkthrough
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DRAGEN Protein Quantification
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Illumina Connected Multiomics provides interactive visualizations and powerful statistics.
For product information, please reference the ICM product documentation
This is a walkthrough of an analysis that could be done on ICM with an example proteomic data set, produced by DRAGEN Protein Quantification. It covers the following features:
Adding samples to a study
Filtering samples
Data Transformation
t-SNE
PCA
Hierarchical clustering and creating heatmaps
Differential expression
Gene set enrichment analysis
Create two sample groups - one for female samples, and one for male samples. Filter by 'Sex', and select 'F' only. At the top of the page, click on ‘+ Create sample group’ to create a sample group.
Type in a group name and click on the ‘Create’ button. Then, change the filter from 'F' to 'M', and create another sample group.
Navigate to the ‘Sample Group’ tab and confirm the sample groups.
Click on ‘+ New Analysis’ to create a new analysis.
In the pop-up window, provide a name for the analysis, select ‘Advanced Analysis’ as the Analysis Type, choose the sample group to be included in the analysis ('All ADAT Samples' will be selected by default), and click on the ‘Run Analysis’ button.
Note: make sure there are no duplicated Sample IDs in the analysis groups.
A pop-up message will show up if the analysis creation is successful.
Refresh the page to get the latest status of the analysis.
When the Status is ‘Complete’, click on the analysis tile to enter the analysis module.
Click on the ‘Sex in F, M’ node generated from the previous step; select ‘Normalization and scaling’ > ‘Normalization’ in the right-hand-side Quantification toolbar.
In the normalization view, drag and drop ‘Add’ and ‘Log’ from the ‘Available methods’ column to the 'Selected methods' column. Select 10.0 in the dropdown menu for ‘Log’, and click on the ‘Finish’ button.
Click on the ‘Normalized counts’ node generated from the data transformation step, and select ‘Exploratory analysis’ > ‘PCA’ in the right-hand-side toolbar.
In the PCA view, adjust the corresponding PCA parameters and click on the ‘Finish’ button.
Compute biomarkers contributing to sex variable:
Click on the ‘PCA’ node generated from the previous step, then select ‘Statistics’ > ‘Compute biomarkers’.
In the compute biomarkers view, select corresponding parameters and click on the ‘Finish’ button. Double click on 'Biomarkers' to explore further.
Click on the ‘PCA’ node generated from the previous step, and select ‘Exploratory analysis’ > ‘t-SNE’ in the right-hand-side toolbar.
In the t-SNE view, select corresponding parameters and click on the ‘Finish’ button. Select the t-SNE node for additional information.
Click on the ‘Normalized counts’ node generated from the data transformation step. In the right-hand-side toolbar, select ‘Statistics’ > ‘Differential analysis’.
In the differential analysis view, select a method (using Limma-trend in this example) and click on the ‘Next’ button.
Select the factors to be included in the model and click on the ‘Add factors’ button. Confirm the selected factors show up in the ‘Selected factor(s)’ section, and proceed to the ‘Next’ button.
Note: For paired or longitudinal analysis, check ‘Random’ column for certain variables to make them a random effect in the mixed effect model.
If a categorical factor is selected (for example, 'Sex'), the next step will be to 'Define comparisons'. Add ‘F’ to the Numerator box and add ‘M’ to the Denominator box. Click ‘Add comparison’ button to create this comparison.
The specified comparisons will appear in the ‘Comparisons’ section. Click on the ‘Finish’ button.
Filtering significant differential analysis results
Click on the ‘F vs M’ node generated from the differential analysis step, and in the right-hand-side of toolbar, select ‘Filtering’ > ‘Differential analysis filter’.
Select ‘Metadata’ as the filter type, and specify the filter criteria. In this example, include features with false discovery rate (FDR) less than 0.05 for the Female vs Male comparison. Double click the filtered feature list node to view the Limma-trend report.
Click on the ‘Filtered feature list’ node generated from the filtering differential analysis results step, and select ‘Exploratory analysis’ > ‘Hierarchical clustering / heatmap’ from the right-hand-side toolbar.
In the Hierarchical clustering / heatmap view, select ‘Assign order’ by Sex in the Sample order section, and click on the ‘Finish’ button.
Double click on the ‘Hierarchical clustering / heatmap’ node generated from the previous step to view the heatmap with hierarchical clustering.
In the left-hand-side toolbar, select ‘Annotations’ and add 'Sex' as a row annotation.
View the heatmap - two major clusters can be observed: a cluster of protein signals with higher abundance in females compared to males, and a cluster with higher abundance in males compared to females.
Select ‘Save as’ in the left-hand-side toolbar to save the heatmap to the data viewer.
Click on the ‘Filtered feature list’ node generated from the filtering differential analysis results step, and select ‘Biological interpretation’ > ‘Gene set enrichment’ in the right-hand-side toolbar.
Select ‘KEGG data base’ in the Database section and select a corresponding library version. If none are available, click "New library" and the respective species. Then, click on the ‘Finish’ button.
Filter Samples and Create Sample GroupsFilter out samples that were flagged during QC during secondary analysis by filtering with the ‘Row Pass’ filter. Optionally filter out samples with other metadata by selecting the corresponding inclusion/exclusion criteria in the right-hand-side ‘Filters’ tab.​​
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For additional information on PCA, review the following documentation:
For additional information on t-SNE, view the following documentation:
For additional information on hiearcharical clustering, view the following documentation:
Double click on the ‘Pathway enrichment’ node generated from the previous step to check the analysis results. Options to download the result table and gene sets are available.
Click on the hyperlinks in the ‘Gene set’ column to view the related genes in a certain pathway.
To view a subset of pathways in the data viewer, filter the pathways further (for example, by filtering for pathways with a P-values of less than 5e-8) and repeating the above gene set enrichment steps on the filtered list. There will be an option to use the data viewer.
