Single Cell on Polly

Harmonize in-house and public single-cell RNA-seq datasets to the highest quality into ML-ready formats fit for diverse analysis methods and pipelines.

Technology

How Does Single-cell Data on Polly Become ML-ready?

Why Harmonize Single-cell Data on Polly?

Configure Curation to Fit Your Analysis Needs

Request extra metadata fields, use custom ontologies, or annotate cell types with your preferred marker database.

Consistently process, annotate, and QC single-cell data using scientifically validated Polly pipelines to ensure data interoperability.

Unify and Manage Data From In-House Assays

Seamlessly integrate Polly into your existing infrastructure! Automate ingestion of in-house data from your data storage (ELN, S3 bucket, CROs, and more) into a central Atlas on Polly.

Focus on discovery, not data wrangling! Polly automatically cleans, harmonizes, and structures your in-house single-cell 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 both in-house and public single-cell data on Polly's secure cloud.

Use Data You Can Trust

All single-cell datasets delivered by Polly undergo ~50 QA checks to ensure quality and provenance.

Assess the intrinsic quality of the data (genes, cells, measurements) with comprehensive QA reports detailing the processing methodology.

Work With Data in Flexible Ways

Avail unrestricted data connectivity and consumption between Polly and your preferred analysis environments. Use APIs to stream harmonized data on Polly to external tools and applications.

Polly Verified – Our Quality Guarantee

We use at least ~50 QA Checks to ensure every dataset is:

Complete

Data validation checks ensure that all cell & dataset-level metadata annotations contain non-NULL and non-blank values.

Accurate

Rigorous QC checks to ensure metadata attributes are human-readable and  accurately assigned at all levels (dataset, cell).

Consistent

Normalization & Batch Effect correction are applied wherever necessary to eliminate technical variations and enable meaningful comparisons between cells.

Distinct

Doublets, which can arise during sample preparation and confound analysis, are identified and removed.

Relevant

Poor quality cells and genes are filtered out. We also identify highly variable genes that drive biological variation and use them for downstream analyses, improving the robustness of results.

Case Studies

Pharma Company Achieves 4x Faster Target Identification for Inflammatory Disease

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