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.
Polly enhances all data by incorporating critical metadata, ensuring uniform processing and harmonization with controlled vocabulary.
.svg)
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.
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.
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.
.svg)
Reduce 80% of your spent in scouring public databases to derive up/ downregulated genes or pathways across indications.
%20(1).svg)
Swiftly detect potential pitfalls associated with identified targets from the outset even before conducting validation experiments.
%20(1).svg)
Dissect study result variations, identify sources of heterogeneity, and prevent bias risks that come from ‘mixing apples and oranges data’ with Polly.
Lorem ipsum dolor sit amet consectetur. Dictumst faucibus nibh imperdiet phasellus vitae ut sit. Ut eros amet massa tellus orci. Vestibulum ac arcu est nulla non eget nulla. Eget pulvinar eu ac mi cursus elementum neque. Massa nisl fringilla platea diam faucibus nullam. In lacus mauris nec ultrices. Ut accumsan leo adipiscing montes proin.
Elucidata’s Polly harmonizes public omics data by integrating datasets from various sources, standardizing metadata, and applying machine-learning-driven quality checks. Polly ensures that raw, unstructured data is transformed into analysis-ready, high-quality datasets, enabling researchers to derive meaningful biological insights.
Lorem ipsum dolor sit amet consectetur. Dictumst faucibus nibh imperdiet phasellus vitae ut sit. Ut eros amet massa tellus orci. Vestibulum ac arcu est nulla non eget nulla. Eget pulvinar eu ac mi cursus elementum neque. Massa nisl fringilla platea diam faucibus nullam. In lacus mauris nec ultrices. Ut accumsan leo adipiscing montes proin.
Polly applies automated pipelines that:
Lorem ipsum dolor sit amet consectetur. Dictumst faucibus nibh imperdiet phasellus vitae ut sit. Ut eros amet massa tellus orci. Vestibulum ac arcu est nulla non eget nulla. Eget pulvinar eu ac mi cursus elementum neque. Massa nisl fringilla platea diam faucibus nullam. In lacus mauris nec ultrices. Ut accumsan leo adipiscing montes proin.
Polly allows users to define and structure custom metadata fields based on research objectives. Researchers can:
Lorem ipsum dolor sit amet consectetur. Dictumst faucibus nibh imperdiet phasellus vitae ut sit. Ut eros amet massa tellus orci. Vestibulum ac arcu est nulla non eget nulla. Eget pulvinar eu ac mi cursus elementum neque. Massa nisl fringilla platea diam faucibus nullam. In lacus mauris nec ultrices. Ut accumsan leo adipiscing montes proin.
Researchers commonly encounter data heterogeneity, missing metadata, batch effects, and standardization issues when performing meta-analyses on public biomedical data. Polly overcomes these by:
Lorem ipsum dolor sit amet consectetur. Dictumst faucibus nibh imperdiet phasellus vitae ut sit. Ut eros amet massa tellus orci. Vestibulum ac arcu est nulla non eget nulla. Eget pulvinar eu ac mi cursus elementum neque. Massa nisl fringilla platea diam faucibus nullam. In lacus mauris nec ultrices. Ut accumsan leo adipiscing montes proin.
Polly streamlines signature discovery by:
Lorem ipsum dolor sit amet consectetur. Dictumst faucibus nibh imperdiet phasellus vitae ut sit. Ut eros amet massa tellus orci. Vestibulum ac arcu est nulla non eget nulla. Eget pulvinar eu ac mi cursus elementum neque. Massa nisl fringilla platea diam faucibus nullam. In lacus mauris nec ultrices. Ut accumsan leo adipiscing montes proin.
Yes. Polly is designed to process a wide variety of data formats—such as FASTQ, TSV, CSV, and JSON—while accommodating different syntaxes and schemas. Its interoperability framework ensures seamless integration across diverse experimental outputs, enabling efficient downstream analysis.
Lorem ipsum dolor sit amet consectetur. Dictumst faucibus nibh imperdiet phasellus vitae ut sit. Ut eros amet massa tellus orci. Vestibulum ac arcu est nulla non eget nulla. Eget pulvinar eu ac mi cursus elementum neque. Massa nisl fringilla platea diam faucibus nullam. In lacus mauris nec ultrices. Ut accumsan leo adipiscing montes proin.
Polly uses machine learning-based batch effect correction techniques, such as:
Lorem ipsum dolor sit amet consectetur. Dictumst faucibus nibh imperdiet phasellus vitae ut sit. Ut eros amet massa tellus orci. Vestibulum ac arcu est nulla non eget nulla. Eget pulvinar eu ac mi cursus elementum neque. Massa nisl fringilla platea diam faucibus nullam. In lacus mauris nec ultrices. Ut accumsan leo adipiscing montes proin.
By providing curated, high-quality, and well-annotated omics datasets, Polly ensures that researchers:
Lorem ipsum dolor sit amet consectetur. Dictumst faucibus nibh imperdiet phasellus vitae ut sit. Ut eros amet massa tellus orci. Vestibulum ac arcu est nulla non eget nulla. Eget pulvinar eu ac mi cursus elementum neque. Massa nisl fringilla platea diam faucibus nullam. In lacus mauris nec ultrices. Ut accumsan leo adipiscing montes proin.
Public databases often have incomplete metadata, inconsistent annotations, and inherent biases. Polly addresses these issues by standardizing metadata across studies, identifying and filtering out biased datasets, and ensuring cross-cohort comparability for more accurate conclusions.
Lorem ipsum dolor sit amet consectetur. Dictumst faucibus nibh imperdiet phasellus vitae ut sit. Ut eros amet massa tellus orci. Vestibulum ac arcu est nulla non eget nulla. Eget pulvinar eu ac mi cursus elementum neque. Massa nisl fringilla platea diam faucibus nullam. In lacus mauris nec ultrices. Ut accumsan leo adipiscing montes proin.
Polly eliminates the need for manual data retrieval by:
Lorem ipsum dolor sit amet consectetur. Dictumst faucibus nibh imperdiet phasellus vitae ut sit. Ut eros amet massa tellus orci. Vestibulum ac arcu est nulla non eget nulla. Eget pulvinar eu ac mi cursus elementum neque. Massa nisl fringilla platea diam faucibus nullam. In lacus mauris nec ultrices. Ut accumsan leo adipiscing montes proin.
To request a demo of Polly:
.svg)
.svg)
.svg)
%201%20(1).svg)
%20(1).svg)
%201%20(1).svg)
.svg)
%201.svg){kind=icon}
.svg)
.svg)
