Illumina Connected Multiomics Walkthrough
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DRAGEN Protein Quantification
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Illumina Connected Multiomics provides interactive visualizations and powerful statistics. This is a walkthrough of an analysis that could be done in Connected Multiomics with an example proteomic data set, produced by DRAGEN Protein Quantification. It covers the following features:
Creating a default analysis
Creating a custom analysis
Create a feature list to filter by
IE, 9.5k human protein list
Managing sample metadata
Filtering samples
Filtering features
Data Transformation
PCA
Differential expression
Hierarchical clustering and creating heatmaps
Gene set enrichment analysis
The SOMAmer content by defaults includes all SOMAmers counted during secondary analysis, including controls and non-human proteins. You may want to exclude some SOMAmers from tertiary analysis. One way to do this is to create a saved list of proteins.
Demo data that can be used to follow along with this walkthrough is found in the Connected Multiomics Demo Data repository. The dataset can be found at /Multiomics-Demo-Data/Proteomics/NovaSeq 6k-S4 Cancer-Normal when adding data in the "Samples" tab. For this study, both SampleType (CRC/Control) and TimePoint (T1...T8) are used. This data must be ingested prior to starting the analysis. Add both the ADAT (counts) and TSV (metadata) to the study.
Click on '+ New Analysis'.
In the pop-up window, provide a name for the analysis, select ‘Default 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.
Click on ‘+ New Analysis’.
In the pop-up window, provide a name for the analysis, select ‘Custom 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.
There is no default initiated analysis for the custom proteomic data. To review the number of samples and features, hover over the data node.
The SOMAmer content for analysis by default includes all SOMAmers counted during secondary analysis, including SOMAmer controls and non-human proteins. You may want to exclude some SOMAmers from tertiary analysis. One way to do this is to create a saved list of proteins.
Click on the setting icon on the top right corner of the analyses dashboard and click on 'Settings' from the menu.
On the settings page, click on 'Lists' from the left hand navigation bar, and then click on '+ New list' on the top of the right panel to add new list.
On the 'Local file' tab, click on '+ Choose' to select the local file and enter the name of the list in the 'Name' box; click on 'Add list' button to upload the list.
During the following analysis, the attached 9.5k human protein list is used. It was generated by filtering SOMAmers by Organism = Human (or only the SOMAmers associated with human proteins) and isolating the Entrez Gene Symbols.
Click on 'Metadata' tab to view and add sample metadata.
Click on 'Manage' under 'Sample attributes' to reorder the metadata. Drag 'SampleStatus' and 'TimePoint' boxes to the front since they are the features that need to be colored for the downstream analysis. You can also add/remove/reorder other metadata or add new category to the current metadata in this page.
Return to the analysis page, click on the 'Quantification' node, choose 'Filtering' > 'Filter samples' from the right hand tool box.
Select the samples with TimePoint T1 and T2.
Click on 'Finish' and return to the analysis page. Click on the 'TimePoint in T1,T2' node, choose 'Filtering' > 'Filter features' from the right hand tool box.
If enabled earlier, select 'Saved list' option, choose the '9.5K human protein - Gene IDs' from the dropdown, and make sure the "Feature identifier" is set to Entrez Gene Symbols; click 'Finish'. Alternatively, upload a manual list of features.
Click on the 'Filtered counts' node, choose 'Normalization and Scaling' > 'Normalization' from the right hand tool box.
Choose 'Add' and drag it to the right-hand box to avoid 0 counts. Then click on 'Finish' to return to the analysis dashboard.
Click on the 'Normalized counts' node, select 'Exploratory analysis' > 'PCA' from the right hand tool box.
Use the default setting and click 'Finish'
Double click on the 'PCA' node to view the PCA report.
The scatter plot shows the data distribution (colored by SampleStatus) among the first three PCs.
The scree plot (top right panel) shows the variant represented by each PC.
The component loading table (bottom right panel) shows the correlation between every protein/SOMAmer and each PC.
Click on the 'Normalized counts' node, select 'Statistics' > 'Differential analysis' from the right hand menu.
Select 'Limma-trend' (default) method and click 'Next'.
Select 'SampleStatus', 'TimePoint', 'DonorID' then click 'Add factors'. Select 'SampleStatus' and 'TimePoint' then click on 'Add interaction' to add the factors. Click on 'Next' to set up comparisons.
Drag 'CRC' to the top right box and 'Control' to the bottom right box. Click on 'Add comparison'. Then Select 'SampleStatus*TimePoint' from the Factor dropdown menu. Add T1 and T2 comparison between CRC and Control. Click on 'Finish' bottom at the bottom.
Double click on the 'Limma-trend' node to view the report. On the left hand menu,
select 'FDR', choose 'Per contrast' and specify 0.05 for CRC vs Control comparison
select 'Fold change', choose 'Per contranst' and specify -2 to 2 for CRC vs Control comparison
click on 'Generate Filtered Node'
repeat this process on CRC T1 vs Control T1 comparison and CRC T2 vs Control T2 comparison
Return to the analyses dashboard and there will be 3 filtered feature list nodes added to the pipeline; right click on the 'Filtered feature list' node and click on 'Rename data node' to rename the node as 'T vs N'; apply the same procedure to the other two filtered feature lists and rename them as 'T vs N Time 1' and 'T vs N Time 2' respectively.
To compare the filtered feature lists, click on the 'Venn diagram' on the bottom menu and tick on the filtered lists ('T vs N', 'T vs N Time 1' and 'T vs N Time 2'); then click on 'Display selection' button on the bottom to visualize the Venn diagram.
Click on 'T vs N' data node, select 'Exploratory analysis' > 'Hierarchical clustering / heatmap' from the right hand tool box.
Choose 'Heatmap' and select the feature order and sample order.
Choose 'Cluster' (default) as feature order
Choose 'Assign order' and select 'SampleStatus' from the dropdown menu.
Click on 'Finish' button at the bottom of the page.
Double click on the 'Hierarchical clustering / heatmap' node to view it.
Click on 'T vs N' data node, select 'Biological interpretation' > 'Gene set enrichment' from the right hand tool box.
Select 'KEGG database', and specify the background gene list as the previously uploaded list of gene symbols. Specifying this list ensures that only genes with associated SOMAmers are included in this analysis. Click on 'Finish' button at the bottom of the page.
Double click on the 'Pathway enrichment' node to view the enriched pathways.
Click on the pathway name to view the pathway network
To download genes in each pathway, click on the value in 'Genes in set' column in the corresponding pathway entry.
To detect differential pathways between diseased and control samples, click on the 'Normalized counts' node and select 'Biological interpretation' > 'GSEA' from the right hand tool box.
Select 'KEGG database' (default) and click on 'Next' button at the bottom of the page.
Select 'SampleStatus' and click on 'Next'.
Drag 'CRC' to the top right box and 'Control' to the bottom right box; click on 'Add comparison'; click 'Finish' on the bottom of the page
Double click on the 'GSEA' node to view the results.
Click on the enrichment plot icon after each row index to visualize the enrichment score of the corresponding pathway.
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For additional information on PCA, review the following documentation:
For additional information on hierarchical clustering, view the following documentation:
