You can compare up to four previously created individual cohorts, to view differences in variants and mutations, RNA expression, copy number variation, and distribution of clinical attributes. Once comparisons are created, they are saved in the Comparisons left-navigation tab of the Cohorts module.
Select Cohorts from the left-navigation panel.
Select 2 to 4 cohorts already created. If you have not created any cohorts, See Create a Cohort documentation.
Click Compare Cohorts in the right-navigation panel.
Note you are now in the Comparisons left-navigation tab of the Cohorts module.
In the Charts Section, if the COHORTS item is not displayed, click the gear icon in the top right and add Cohorts as the first attribute and click Save.
The COHORTS item in the charts panel will provide a count of subjects in each cohort and act as a legend for color representation throughout comparison screens.
For each clinical attribute category, a bar chart is displayed. Use the gear icon to select attributes to display in the charts panel.
You can share a comparison with other team members in the same ICA Project. Please refer to the section on "Sharing a Cohort" on "Create a Cohort" for details on sharing, unsharing, deleting, and archiving, which are analogous for sharing comparisons.
Select the Attributes tab
Attribute categories are listed and can be expanded using the down-arrows next to the category names. The categories available are based on cohorts selected. Categories and attributes are part of the ICA Cohorts metadata template that map to each Subject.
For example, use the drop-down arrow next to Vital status to view sub-categories and frequencies across selected cohorts.
Select the Genes tab
Search for a gene of interest using its HUGO/HGNC gene symbol
Variants and mutations will be displayed as one needle plot for each cohort that is part of the comparison (see Cohort analysis -> Genes in this online help for more details)
As additional filter options, you can view only those variants that are occur in every cohort; that are unique to one cohort; that have been observed in at least two cohorts; or any variant.
Select the Survival Summary tab.
Attribute categories are listed and can be expanded using the down-arrows next to the category names.
Select the drop-down arrow for Therapeutic interventions.
In each subcategory there is a sum of the subject counts across select cohorts.
For each cohort, designated by a color, there is a Subject count and Median survival (years) column.
Type Malignancy in the Search Box and an auto-complete dropdown suggests three different attributes.
Select Synchronous malignancy and the results are automatically opened and highlighted in orange.
Click Survival Comparison tab.
A Kaplan-Meier Curve is rendered based on each cohort.
P-Value Displayed at the top of Survival Comparison indicates whether there is statistically significant variance between survival probabilities over time of any pair of cohorts (CI=0.95).
When comparing two cohorts, the P-Value is shown above the two survival curves. For three or four cohorts, P-Values are shown as a pair-wise heatmap, comparing each cohort to every other cohort.
Select the Marker Frequency tab.
Select either Gene expression (default), Somatic mutation, or Copy number variation
For gene expression (up- versus down-regulated) and for copy number variation (gain versus loss), Cohorts will display a list of all genes with bidirectional barcharts
For somatic mutations, the barcharts are unidirectional and indicate the percentage of samples with a mutation in each gene per cohort.
Bars are color-coded by cohort, see the accompanying legend.
Each row shows P-value(s) resulting from pairwise comparison of all cohorts. In the case of comparing two cohorts, the numerical P-value will be displayed in the table. In the case of comparing three or more cohorts, the pairwise P-values are shown as a triangular heatmap, with details available as a tooltip.
Select the Correlation tab.
Similar to the single-cohort view (Cohort Analysis | Correlation), choose two clinical attributes and/or genes to compare.
Depending on the available data types for the two selections (categorical and/or continuous), Cohorts will display a bubble plot, violin plot, or scatter plot.