Identify molecular signatures from meticulously curated and high-quality omics data on Polly.
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.
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Polly enhances all data by incorporating critical metadata, ensuring uniform processing and harmonization with controlled vocabulary.
Process results across multiple platforms (Microarray, Bulk RNA-seq, and scRNAseq), overcome batch effects and make all data comparable.
Address custom metadata, and cohorting needs with Polly’s scalable harmonization.
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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.
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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.
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Reduce 80% of your spent in scouring public databases to derive up/ downregulated genes or pathways across indications.
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Swiftly detect potential pitfalls associated with identified targets from the outset even before conducting validation experiments.
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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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