Harmonize in-house and public single-cell RNA-seq datasets to ML-ready formats and leverage Polly’s suite of custom solutions designed for scRNA-seq data, to accelerate analysis and insight generation.
Polly harmonizes unstructured single-cell data with a configurable, transparent, and granular curation process as per your inclusion/ exclusion criteria to accelerate downstream analysis.
Polly’s datasets come with 99.99% accuracy and have ontology-backed metadata for 30+ fields.
Polly delivers datasets with 100% metadata completeness and 0 empty metadata fields.
Make single-cell data from disparate sources interoperable & analysis-ready with our datatype-agnostic data model.
Integrate multi-modal datasets into one central Atlas to unveil hidden patterns, and expedite research breakthroughs.
Access unfiltered raw counts from original publications on Polly, or get consistent Polly-processed single-cell data or replicate author-defined counts, as per your research needs.
Use Polly for custom cell type annotations for your single-cell data using markers derived from subclusters or figures in the publication.
Single-cell datasets on Polly go through a robust ~50 steps QA/QC check to ensure the metadata quality, filtering and normalization, batch effect correction, as well as quality of measurements.
Extract deeper insights from data at hand by using Polly’s extensive suite of ML solutions to accelerate downstream analysis.
Work with our experts to deploy popular foundational models like scGPT across your own harmonized data, or fine-tune existing models to improve predictions or accelerate insights.
Work with tools for constructing and analyzing cell-cell interaction networks, providing insights into cellular communication and signaling pathways. Perform analysis like differential expression, trajectory analysis, UMAP, clustering or more.
Run queries across your harmonized data using Polly-GPT, a natural language-based querying interface, to perform complex statistical analyses like PCA and differential gene expression.
Leverage our expertise to construct tailored consumption methods, unique to your research.
Use native web-apps integrated on Polly- like CellxGene and CellxGene VIP to analyze and visualize an array of single-cell data on the fly.
Use Polly’s APIs or GUI to stream harmonized data on external tools like Spotfire, or your preferred analysis environment like react, shiny, etc. Or work with our experts to build or customize a production-ready, scientifically validated application that caters to your research needs.
Develop methods for integrating single-cell data with other omics data types (e.g., genomics, proteomics) to gain a more comprehensive understanding of cellular processes.
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.
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.
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.
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.
We use at least ~50 QA Checks to ensure every dataset is:
Data validation checks ensure that all cell & dataset-level metadata annotations contain non-NULL and non-blank values, all metadata attributes are human-readable and accurately assigned at all levels.
Normalization & Batch Effect correction are applied wherever necessary to eliminate technical variations and enable meaningful comparisons between cells.
Polly filters out poor-quality cells and genes and also identifies highly variable genes that drive biological variation useful for downstream analyses, and for improving the robustness of results.
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We offer 30+ curated fields for single-cell RNA-seq datasets on Polly. If any additional curated fields are required, they are added on request as part of custom curation.
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Single-cell datasets are stored in the H5AD format on Polly. Additionally, our team can also support custom requests for providing data in the file formats that are best suited for the downstream bioinformatics tools and pipelines used by our clients.
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.
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.
Single-cell data on Polly has various benefits,
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.
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.
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, our team has the expertise to provide integrated single-cell datasets on Polly upon request. We don't follow a specific method for integration. The integration methodology is architected by our team of experts based on the biological question and the downstream analysis our client wants to perform.
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