> For the complete documentation index, see [llms.txt](https://help.bioinsight.illumina.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://help.bioinsight.illumina.com/icm/analyses/analysis-functionality/task-menu/biological-interpretation/correlation-engine-meta-analysis.md). # Correlation Engine meta-analysis ## What is Correlation Engine meta-analysis? Validate any molecular signature in your Connected Multiomics analysis against Correlation Engine (CE) curated studies (> 26,700) using the Correlation Engine meta-analysis task to determine whether findings are validated across independent datasets. This task lets you ask: *Do my findings replicate across thousands of independent studies?* ## Performing the CE meta analysis task * Single-click any gene/protein signature from these types of result nodes: differential analysis results (feature lists), QTL hits, MOFA factor loadings, SNF cluster markers, WGCNA module genes, or manually curated gene lists * In the context-sensitive toolbox select **Biological interpretation** > **Correlation Engine meta analysis**

Select the **Correlation Engine meta analysis** task from the toolbox

* Select the **Organism** using the drop-down or begin typing the name * Select the **Correlation Engine project** or choose Other to enter a custom project name * Enter a **Correlation Engine study name** that will be created for the analysis within the selected project * Click **Next**

* **Select one or more contrasts** to use for Correlation Engine analysis. If multiple contrasts are selected, an analysis will be performed for each contrast and an enrichment table will be produced for each contrast * **Select feature identifier** by choosing the column that contains the gene identifiers to use for the analysis * Click **Next**

* (Optional) Choose to filter the results by organism or data type * Leave **Filter by organism** and **Filter by data type** unchecked to include all organisms and data types in the results. * Click **Finish**

Result configuration for the task. Leave all filters unchecked to include all organisms and data types in the results

The task produces a Correlation Engine meta analysis results node (circle)

## Viewing the Correlation Engine meta analysis results * Double-click to open the Correlation Engine meta analysis task report rank-ordered results.

The table can be **downloaded** by clicking

. The columns can be **sorted** and **filtered** using text and operators such as >= or <= in the numeric columns. Clicking a **Bioset ID** will take you to the Bioset in Correlation Engine and clicking a **Study ID** will take you to the study in CE. Illumina has developed the *Running Fisher* algorithm for CE use. See more details in our technical note: [Data Correlation Details: Enrichment Analysis](https://www.illumina.com/content/dam/illumina-marketing/documents/products/technotes/technote-data-correlation-enrichment.pdf) **P-value** is the bioset-bioset *Running Fisher* p-value **Score** is the ln(p-value) **Normalized score** is the SCOREmax / SCOREi \* 100 **Correlation** is the overall direction of the majority of genes as either positive/up or negative/down The results can be viewed in the Data viewer by clicking

[More information for Meta-Analysis questions in Correlation engine.](https://help.correlationengine.illumina.com/correlation-engine-faq#advq) --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://help.bioinsight.illumina.com/icm/analyses/analysis-functionality/task-menu/biological-interpretation/correlation-engine-meta-analysis.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.