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.
When results vary across datasets, conditions, or omics layers, it can be difficult to tell what’s meaningful and what’s noise. iPathwayGuide™’s Meta-Analysis helps you cut through the complexity and focus on what truly matters.
“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
Key Meta-Analysis Benefits
- Integrate different data types to reveal common or unique traits
- Compare relative ranks across conditions and time points
- Identify shared mechanisms or distinct molecular signatures
- Pinpoint plausible biomarkers with higher confidence
“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
About Advaita Bioinformatics
Advaita Bioinformatics develops bioinformatics tools that integrate multi-omic data using a systems biology approach. Our proprietary computational method, Impact Analysis, goes beyond traditional enrichment methods by leveraging the type, function, position, and interactions of genes within pathways. Our software helps principal investigators, core facilities, and enterprise bioinformatics teams analyze gene expression data (e.g., RNA-Seq or microarray) and variant data (e.g., DNA-Seq) to discover biomarkers, identify impacted pathways, uncover underlying mechanisms, and more.
Multi-Omics Pathway Analysis
From bulk RNA-seq to single cell, from proteomics to epigenetics, our platform delivers powerful insights. Biologists depend on our state-of-the art pathway analysis, gene ontology analysis, upstream regulator predictions, and meta-analysis.
