> 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/correlation-engine/querying-and-interpreting-results/meta-analysis.md). # Meta-Analysis **Meta-Analysis** enables you to query a collection of multiple biosets simultaneously to derive a consensus gene signature and discover commonly regulated pathways. Rather than analysing one experiment at a time, Meta-Analysis synthesises patterns across up to 150 biosets, revealing the most consistently regulated genes and enriched biogroups. ## When to Use Meta-Analysis Meta-Analysis is most valuable when you want to: * Identify genes that are consistently regulated across multiple experiments or conditions. * Find pathways enriched across a disease programme or experimental series. * Compare your private data against the CE public library using a multi-bioset query. ## Adding Biosets to Meta-Analysis You can add biosets to Meta-Analysis from anywhere in CE: 1. Navigate to a study of interest (from My Studies, Curated Studies, or any app results page). 2. Click the **Meta-Analysis icon** ![](https://1165879419-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FWolK2fDgPCCcGurM8A73%2Fuploads%2Fgit-blob-5586a2ab1b202be9ea5e5867c588315204e1431d%2Fimage%20\(2\).png?alt=media) to the left of any bioset name. The icon updates to confirm the bioset has been added and the counter in the app menu increments. 3. Continue browsing — Meta-Analysis remembers all added biosets across page navigation. CE remembers your current Meta-Analysis until you sign out. To start a new query, click **Remove All** from the Meta-Analysis setup box. ## Running Meta-Analysis 1. Click the **large Meta-Analysis icon** ![](https://1165879419-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FWolK2fDgPCCcGurM8A73%2Fuploads%2Fgit-blob-5586a2ab1b202be9ea5e5867c588315204e1431d%2Fimage%20\(2\).png?alt=media) in the app menu to go to the **Setup tab**. 2. Review the list of added biosets — remove any you do not want to include, or drag-and-drop to reorder them. 3. Click **Run** to generate results. **Query limits:** * Up to **150 biosets** for a standard gene/biogroup/bioset results query. * Up to **10 biosets** to create a Meta-Analysis query that searches all CE biosets for correlating signatures. Biosets can span different platforms, organisms, projects, and libraries. ## Interpreting Meta-Analysis Results Results are organized into four tabs: ### Gene Results Tab The most consistently and highly regulated genes across your input biosets. Each gene row includes a **score matrix** (colored bar chart): * The height of each bar represents the correlation score for that bioset. * **Red bar above midline**: positive correlation (gene is up-regulated or amplified in that bioset). * **Green bar below midline**: negative correlation (gene is down-regulated or deleted). * **Orange bar above midline**: non-directional correlation (e.g., SNP or mutation biosets). * Absent bar: correlation is not significant for that bioset. (Only the top 5,000 features in each queried bioset are considered to reduce noise.) ### Biogroup Results Tab Enriched biogroups (Gene Ontology, MSigDB, etc.) that are consistently associated with your bioset collection. The score matrix interpretation is the same as gene results, but each bar represents enrichment with up-regulated (red) or down-regulated (green) genes from each queried bioset. ### Bioset Results Tab Public or private CE biosets that are correlated with your input collection. Each correlated bioset has a score matrix showing the strength and direction of its correlation with each of your input biosets. ### SNP Results Tab Available if at least one input bioset contains SNP or mutation data. Shows SNPs correlated with your input collection. ## Visualizations (Heatmaps) Meta-Analysis Visualizations render interactive heatmaps of the Gene Results or Biogroup Results using R/Heatmaply. Requirements: * Minimum 2, maximum 5 biosets * Supported data types: gene expression, protein expression, DNA methylation, ATAC-seq * Displayed results limited to top 500 consensus-ranked genes or biogroups * Full data available via the Export button on the respective results tab ## Saving and Exporting Results * **Export Results** — downloads the full results table as an Excel file. * **Bookmark** — save the results page URL for later access (CE will retain the state). ## Meta-Analysis API Correlation Engine provides an API for programmatic access to Meta-Analysis functionality (available since v2.1.1). Contact for API documentation. --- # 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. 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