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• Click the counts data node
• Click the Correlation Statistics section in the toolbox
• Click Correlation analysis
• Select the factors and interactions to include in the statistical test 
• Correlation 
• Choose the method to use for correlation analysis (Figure 1) 

Feature many-to-one correlation

Only numeric factors are available. When multiple factors are added, the correlation analysis will perform each factor with a feature in the data node independently.

• Select the factors and interactions to include in the statistical test (Figure 2)

• Click Next 
• It is optional to apply a lowest coverage filter or configure the advanced settings
• Click Finish to run

Correlation analysis produces a Feature list a Correlation data node. The ; double-click to open the task report (Figure 13) which is similar to the ANOVA and GSA task reports and includes a table with features on rows and statistical results on columns.

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Each numeric attribute includes p-value, adjusted p-value columns (FDR step up and/or Storey q-value if included), and a partial correlation value. Each interaction will have p-value and adjusted p-value columns (FDR step up and/or Storey q-value if included).

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Sets the type of correlation used to calculate the correlation coefficient and p-value. Options are Pearson (linear), Spearman (rank), Kendall (tau). Default is Pearson (linear). 

Correlation across assays

Correlation across assays should be used to perform correlation analysis across different modalities (e.g. ATAC-Seq enriched regions vs. RNA-Seq expression) for multiomics data analysis. This option will only be available when multiple types of data are available in the project. 

• Select the data node to be compared to the node that the task has been invoked from using the Select data node button
• Modify any parameters (Figure 4)
• Click Finish

Correlation across assays produces a Correlation pair list data node; double-click to open the list. 

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