Unlock the power of spatial transcriptomics with ML-ready data ingested from public databases or your in-house assays. Identify spatially regulated genes and gain deeper insights into disease mechanisms.
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Polly harmonizes Spatial Transcriptomics (SRT) data from diverse public and in-house sources by integrating raw counts matrix, spatial coordinates, imaging data, and metadata seamlessly.
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Access unfiltered raw counts from original publications and get consistently processed spatial transcriptomics data to replicate author-defined counts.
Access spatial datasets with deeply annotated metadata up to 3 levels (dataset, sample, and feature) for in-depth analysis.
Every spatial transcriptomics dataset on Polly goes through ~50 QA/QC checks for metadata quality, filtering and normalization, batch effect correction, and quality of measurements.
Integrate spatial transcriptomics datasets into one central Atlas to unveil cell-type localization patterns, and expedite research breakthroughs.
Streamline QC filtering, normalization, clustering, and spatial variable gene analysis with deconvolution for comprehensive insights.
Polly’s ‘Unified Data Model’ stores diverse datasets within a single relational database, optimizing storage efficiency.
Enable seamless access and queries on top of high-quality harmonized and integrated data with APIs.
Advance research with harmonized spatial datasets with confidence using Polly’s extensive suite of ML solutions.
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Harness cutting-edge graph-based ML algorithms to unveil spatially coordinated gene expression, decoding the language of regulatory networks.
Enhance analysis by integrating spatial information into feature selection. Deploy informed variable selection or clustering algorithms to identify genes or features linked with specific spatial patterns or cell types.
Leverage custom applications and dashboards to visualize cell-type composition, empowering in-depth exploration and interpretation of trends and patterns.
Use native web-apps integrated on Polly - like CellxGene VIP to analyze and visualize an array of spatial transcriptomics data on the fly.
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Tailor your research with flexible bioinformatics pipelines like STAR, Kallisto, and more, on Polly, achieving consistent, cost-effective data processing.
Customize the QC mechanisms, cut-offs, and log-fold thresholds used to guarantee superior data quality throughout the ETL process.
Request additional curation of metadata, cohorts, or comparisons within cohorts to streamline the search for biologically relevant signatures.
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’s AI-assisted curation automatically harmonizes all your data into ML-ready formats, in a fraction of the time.
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.
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Our experts implement ~50 QA checks to perform batch effect correction, metadata validation, and remove technical artifacts & variations in every dataset.
The data normalization methods or QC metrics used on Polly are not a black box. Learn how each Bulk RNA-seq dataset was processed by downloading a detailed QA/QC report from Polly.
Perform gene, pathway, or metadata-based queries to find and explore the data you need.
Utilize interactive volcano plots, heatmaps, and more to visualize enriched genes and pathways.
Stream Polly harmonized Bulk RNA-seq datasets to your preferred tools for advanced analyses.
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We use ~50 QA Checks to ensure every dataset is:
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Data validation checks ensure that all cell & dataset-level metadata annotations contain non-NULL and non-blank values.
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Rigorous QC checks to ensure metadata attributes are human-readable and accurately assigned at all levels (dataset, cell).
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Normalization & Batch Effect correction are applied wherever necessary to eliminate technical variations and enable meaningful comparisons between cells.
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Doublets, which can arise during sample preparation and confound analysis, are identified and removed.
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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.
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