Meta-Analysis of Gene Expression

Made Simple

Turning your complex gene expression datasets into clear, confident conclusions.

Meta-Analysis venn diagram iPathwayGuide™

Overview

Benefits

Science

Functions

Testimonials

What is Meta-Analysis?

Meta-analysis is a method for comparing multiple conditions, time points, or data types in a single, unified analysis. With iPathwayGuide™, you can overlay transcriptomics, proteomics, and other datasets onto the same interactive pathway diagram to spot consistent signaling patterns or compare gene expression across disease variations. You can even track how signaling in a pathway evolves step-by-step over the course of a time-series experiment, revealing dynamic changes that static comparisons might miss. By analyzing up to five contrasts at once through meta-analysis, you can uncover shared mechanisms, unique molecular signatures, and promising biomarkers with greater confidence.

Benefits of Meta-Analysis

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Rank-diagram-iPathwayGuide™-meta-analysis

View the relative ranking of differentially expressed genes for each contrast, based on p-value (PV).

Rank Diagram

Compare gene expressions across all conditions instantly.

Instantly view gene expression across multiple conditions. No need for repeated searches.

Hover over any gene to see how it’s expressed in every condition, all in one view.

Quickly identify the most significantly up- or down-regulated genes per condition to prioritize biologically relevant targets.

Pathway Diagram

Pathways impacted for each condition or all conditions combined.

Visualize how entire pathways are enriched or disrupted in each condition, no manual mapping needed.

Toggle between individual conditions or combined overview for both granular and holistic understanding.

Go from gene lists to functional insight in seconds. Ideal for hypothesis generation and validation.

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View which genes are differentially expressed in a specified pathway, where upregulated genes are displayed in red, and downregulated genes in blue.

Venn Diagram & UpSet Plot

Unlock insights from overlaps across your conditions

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View the number of differentially expressed (DE) genes for each contrast expressed as a Venn Diagram. This allows you to see the count of DE genes shared between contrasts, or unique for a specific condition.

A complement to the Venn Diagram, the UpSet Plot allows you to view the number of differentially expressed genes for the intersection or union of different contrasts.

Identify and extract gene sets based on specific overlaps or exclusions across conditions.

Refine your focus to genes of interest with exact logic (ex. shared across two conditions but not a third), without manually cross-referencing lists.

Quickly understand the relationships among differentially expressed gene sets between conditions. Ideal for studies involving multiple comparisons or treatments.

The Science of Meta-Analysis

iPathwayGuide™: Enabling True Meta-Analysis

iPathwayGuide™ is the only platform that enables true meta-analysis across multiple conditions, time points, and omics data types, all in a single, unified analysis. By comparing up to five datasets simultaneously, you can uncover shared mechanisms, distinct molecular signatures, and plausible biomarkers with higher confidence.

Meta-Analysis offers a powerful, flexible approach to uncovering biological insights across studies. For example, you can visualize multiple data types, including transcriptomics, proteomics, and more, on a single interactive pathway to quickly spot consistent signals. Or bring your time-series data to life with dynamic pathway animations that reveal gene expression changes across multiple time points.

Functions of Meta-Analysis

Integrate

different data types to reveal common or unique traits

Identify

shared mechanisms or distinct molecular signatures

Compare

relative ranks across conditions and time points

Pinpoint

plausible biomarkers with higher confidence

Why do scientists love meta-analysis in iPathwayGuide™?

“iPathwayGuide™ allows me to quickly go through the data, parse out things that are going to be noise, and then really just allow me to identify the most important features of our data sets… I think it gives a great overview of the pathways involved, and it is an easier, more interpretive interface to do a meta-analysis. So, for instance, pull in say four data sets and compare those within each other.”

– Douglas Dluzen, Ph.D, Assistant Professor at Morgan State University