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Correlation analysis is a statistical test that lets you rank features by their correlation with numeric attributes using Pearson (linear), Spearman (rank), or Kendall (tau) correlation. 

 Running Running Correlation analysis

We recommend normalizing you data prior to running Hurdle modelCorrelation analysis, but it can be invoked on any counts data node. 

• Click the counts data node
• Click the Differential analysis Statistics section in the toolbox
• Click Correlation analysisCorrelation 
• Choose the method to use for correlation analysis (Figure 1) 

Feature many-to-one correlation

When multiple numeric factors are added, the correlation analysis will perform each factor with a feature in the data node independently. If you are interested in particular features, use the Search features box to add one or more. 

• Select the factors and interactions to include in the statistical test 

Only numeric factors are available. 

• Click Next 
• 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/LIMMA-trend/LIMMA-voom 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).

Each feature includes  chromosome view,  dot plot,  correlation plot, and extra details  buttons in the View column. 

Correlation analysis advanced options

Low value filter

Low-value filter allows you to specify criteria to exclude features that do not meet the requirements for the calculation. If there is a filter feature task performed in the upstream analysis, the default of this filter is set to None, otherwise, the default is Lowest average coverage is set to 1.

Lowest average coverage: the computation will exclude a feature if its geometric mean across all samples is below than the specified value

Lowest maximum coverage: the computation will exclude a feature if its maximum across all samples is below the specified value

Minimum coverage: the computation will exclude a feature if its sum across all samples is below than the specified value

None: include all features in the computation

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There are situations when a model estimation procedure does not fail outright , but still encounters some difficulties. In this case, it can even generate p-value and fold change on the comparisons, but they are not reliable, i.e. they can be misleading. Therefore, the default of Use only reliable estimation results is set Yes.

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Sets the type of correlation used to calculated 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. 

• 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 analysis options

Correlation and similarity measures

Features within same chromosome: this option will restrict feature comparison to the chromosome location

All features in one data node vs all features in the other data node: this option will perform the comparison using all combinations without location constraint

Pearson: linear correlation: Image Added

Spearman: rank correlation: Image Added

Report correlation pairs

P-value: select a cut-off value for significance and only those pairs that meet the criteria will be reported

abs(Correlation coefficient): select a cutoff for reporting the absolute value of the correlation coefficient (represented by the symbol r) where a perfect relationship is 1 and no relationship is 0

Correlation across assays produces a Correlation pair list data node; double-click to open the table (Figure 5). The table can be sorted and filtered using the column titles. 

Click Image Added View correlation plot to open the correlation plot for each comparison. Scroll to the bottom of the table to Image Added download the full table report. 

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