Harmonize in-house microarray data and find relevant datasets from GEO and Array Express to predict molecular signatures or integrate with other transcriptomic assays using Polly.
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Polly delivers Log2 (X+1) transformed microarray datasets, ready for seamless integration with any ML-model or normalization method.
Ensure complete precision in data with no feature drop - All sample and feature level metadata from the in-house or public source is retained in every Polly harmonized data file.
Customize the curation of metadata fields, cohorts, or comparisons within cohorts to fit unique analysis needs.
Eliminate manual data entry! Automatically ingest microarray data from your workflows (ELN, S3 bucket, CROs, and more) into Polly with our data importers.
Focus on discovery, not data wrangling! Polly automatically cleans, harmonizes, and structures your in-house datasets, ensuring they adhere to your custom schema.
Integrate multi-modal datasets into one central Atlas to unveil hidden patterns, and expedite research breakthroughs.
Effortlessly manage and analyze TBs of in-house and publicly available Microarray data on Polly's secure cloud.
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Rely on our experts for meticulous QA/QC checks, guaranteeing metadata completeness and schema compliance. We eliminate technical artifacts and redundancies, ensuring your data is ready for analysis.
The data annotation methods or QC metrics used on Polly are not a black box. Learn how each microarray dataset was annotated by downloading a detailed QA/QC report from your Atlas on Polly.
Unlock feature-level queries effortlessly with Polly – where complex probe IDs are transformed into meaningful gene symbols, simplifying your analysis for greater insights.
Utilize interactive volcano plots, heatmaps, and more to visualize enriched genes and pathways.
Effortlessly transform selected in-house or public datasets into RDS objects using Polly's R packages and stream them into internal analysis pipelines.
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We use a comprehensive QA/QC checklist to ensure every dataset is:
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Ensure all dataset and sample-level metadata annotations contain non-NULL and non-blank values.
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Ensure metadata attributes are human-readable and accurately assigned at all levels (dataset, sample, and feature).
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Metadata is validated for completeness, standardization, and logical correctness making them consistent and reliable throughout.
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Validate gene IDs, detect duplicates, and ensure valid values in the data matrix for precise scientific analysis.
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Inconsistent or unwanted columns in the source metadata are removed for enhanced precision.
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