Perform Meta-analysis of Harmonized Data on Polly

Identify molecular signatures from meticulously curated and high-quality omics data on Polly.

Meta-analyzing Published Data Is Not Trivial

Meta-analyzing diverse public studies is key to identifying and validating molecular signatures. However, this is not a trivial process. Public biomedical databases are notoriously fragmented and use varying formats, syntaxes, schemas and entity notations.  In this scenario, mining, integrating and harmonizing data becomes a bottleneck.

How Polly Helps?

Highest Quality Data to Arrive at Validated Results

Polly enhances all data by incorporating critical metadata, ensuring uniform processing and harmonization with controlled vocabulary.

Process results across multiple platforms (Microarray, Bulk RNAseq, and scRNAseq), overcome batch effects and make all data comparable.

Address custom metadata, and cohorting needs with Polly’s scalable harmonization.

Discover the Data You Need

Experience 360-degree findability for uncovering novel therapeutic targets. Save ~2X the time spent auditing public sources for accurate data.

Scan public or in-house data sources to arrive at a pool of datasets most relevant to your question.

Search across genes, pathways, indications, etc., using free-text search and contextual filters on a performant GUI.

Generate richer queries using an ontology-based recommendation engine. For example, search results for lung cancer won't just yield keyword matches but also insights into different subtypes.

Unleash the Power of Curated Data

Start analyzing with Polly’s Meta-analysis application.

Pick the right cohorts from your selected pool of datasets using a drag-and-drop cohort builder.

Generate interactive heatmaps, volcano plots, or scatter plots to explore gene expression levels of specific genes across multiple cohorts' biological conditions.

Get a list of meta-analyzed genes or pathways with a built-in random-effect model.

The Polly Difference

Accelerate Critical

Reduce 80% of your spent in scouring public databases to derive up/ downregulated genes or pathways across indications.

Improve Your Hypotheses Success Rate

Swiftly detect potential pitfalls associated with identified targets from the outset even before conducting validation experiments.

Overcome Limitations
and Bias

Dissect study result variations, identify sources of heterogeneity, and prevent bias risks that come from ‘mixing apples and oranges data’ with Polly.

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Case Studies

GEO Datasets for Transcriptomics Meta-Analysis in Research

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