# Correlation analysis * [What is Correlation analysis?](#what-is-correlation-analysis) * [Running Correlation analysis](#running-correlation-analysis) * [Feature many-to-one correlation](#feature-many-to-one-correlation) * [Correlation analysis advanced options](#correlation-analysis-advanced-options) * [Correlation across assays](#correlation-across-assays) * [Correlation across assays analysis options](#correlation-across-assays-analysis-options) ## What is Correlation analysis? *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 Correlation analysis We recommend normalizing you data prior to running *Correlation analysis*, but it can be invoked on any counts data node. * Click the counts data node * Click the **Statistics** section in the toolbox * Click **Correlation** * Choose the method to use for correlation analysis (Figure 1)

Figure 1. Choose the method to use for correlation analysis

## 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 (Figure 2).

Figure 2. Select the factors and interactions to include

* Click **Next** * It is optional to apply a lowest coverage filter or configure the advanced settings * Click **Finish** to run *Correlation analysis* produces a *Correlation* data node; double-click to open the task report (Figure 3) which is similar to the [ANOVA/LIMMA-trend/LIMMA-voom](https://help.partek.illumina.com/partek-flow/user-manual/task-menu/differential-analysis/anova-limma-trend-limma-voom) and [GSA](https://help.partek.illumina.com/partek-flow/user-manual/task-menu/differential-analysis/gsa) task reports and includes a table with features on rows and statistical results on columns.

Figure 3. Correlation analysis task report

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 ![](https://1384254481-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FJVEESmJAPppJ3ijFq5aR%2Fuploads%2Fgit-blob-8ebdae67617d1d607cf906d8826f3f9bb5a8b799%2Fimage%20\(224\).png?alt=media) [chromosome view](https://github.com/illumina-swi/partek-docs/blob/main/docs/partek-flow/user-manual/task-menu/visualizations/chromosome-view/chromosome-view.md), ![](https://1384254481-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FJVEESmJAPppJ3ijFq5aR%2Fuploads%2Fgit-blob-0ab2d4f3b4812ca7a05979c2fc288a2aabd42468%2Fimage%20\(225\).png?alt=media) [dot plot](https://github.com/illumina-swi/partek-docs/blob/main/docs/partek-flow/user-manual/task-menu/visualizations/dot-plot.md), ![](https://1384254481-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FJVEESmJAPppJ3ijFq5aR%2Fuploads%2Fgit-blob-f934764ce9933357beb3b0dd3bf3cf35c4fb592a%2Fimage%20\(226\).png?alt=media) [correlation plot](https://github.com/illumina-swi/partek-docs/blob/main/docs/partek-flow/user-manual/task-menu/visualizations/correlation-plot.md), and extra details ![](https://1384254481-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FJVEESmJAPppJ3ijFq5aR%2Fuploads%2Fgit-blob-2152159f4eb59e07e931ad17f1871338775a614c%2Fimage%20\(227\).png?alt=media) 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 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 the specified value *None*: include all features in the computation #### Multiple test correction Multiple test correction can be performed on the p-values of each comparison, with **FDR step-up** being the default. If you check the *Storey q-value*, an extra column with q-values will be added to the report. #### Use only reliable estimation results 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**. #### Correlation type 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. * 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**

Figure 4. Correlation across assays can be performed with multiomic data

### 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: ![](https://1384254481-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FJVEESmJAPppJ3ijFq5aR%2Fuploads%2Fgit-blob-817b17d1057d994cfb2e37d7c4efd867da7dbf3e%2Fimage%20\(229\).png?alt=media) *Spearman*: rank correlation: ![](https://1384254481-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FJVEESmJAPppJ3ijFq5aR%2Fuploads%2Fgit-blob-d30dba60d31ec3c0a4380b25140767038aed3417%2Fimage%20\(230\).png?alt=media) #### 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.

Figure 5. Correlation across assays table

Click ![](https://1384254481-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FJVEESmJAPppJ3ijFq5aR%2Fuploads%2Fgit-blob-01a7c04d4011c00c5c26ebee6fc776d37817228f%2Fimage%20\(232\).png?alt=media) *View correlation plot* to open the correlation plot for each comparison. Scroll to the bottom of the table to ![](https://1384254481-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FJVEESmJAPppJ3ijFq5aR%2Fuploads%2Fgit-blob-22ce7e18ebe116891d93a67cd80b02b831c7aad9%2Fimage%20\(233\).png?alt=media) download the full table report. ## Additional Assistance If you need additional assistance, please visit [our support page](http://www.partek.com/support) to submit a help ticket or find phone numbers for regional support.