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.
Custom-curated datasets for your unique research objectives.
Complete, consistent, and relevant high-quality datasets.
Analysis-ready datasets with comprehensive quality metrics.
Rich metadata for advanced therapeutic research.
Harmonize spatial transcriptomics data from various sources by seamlessly integrating raw counts matrices, spatial coordinates,
imaging data, and metadata.
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Access unfiltered raw counts from original publications and receive consistently processed data to replicate author-defined counts.
Explore datasets with in-depth metadata annotations across three levels (dataset, sample, and feature) for comprehensive analysis.
Each spatial transcriptomics dataset undergoes ~50 QA/QC checks, ensuring metadata quality, filtering, normalization, batch effect correction, and measurement accuracy.
Integrate spatial transcriptomics datasets into a centralized 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.
The ‘Unified Data Model’ stores diverse datasets within a single relational database, optimizing storage efficiency.
Enable seamless access and queries on high-quality, harmonized, and integrated data through APIs.
Revealing spatial gene networks with ML solutions for coordinated expression analysis.
Utilize cutting-edge graph-based algorithms to uncover spatially coordinated gene expression and decode regulatory networks.
Integrate spatial information into feature selection to identify genes or features linked to specific spatial patterns or cell types.
Leverage our expertise to create custom data consumption workflows tailored to your unique research needs.
Utilize custom applications and dashboards to visualize cell-type composition, empowering in-depth exploration and interpretation of trends and patterns.
Make use of native integrated web apps like CellxGene VIP to analyze and visualize a variety of spatial transcriptomics data in real time.
Integrate spatial transcriptomics with traditional histology for enhanced tissue characterization and diagnostics. Bridge molecular biology with classic morphological studies to achieve more precise diagnostics.
Track how gene expression patterns shift across tissue regions as chronic diseases like fibrosis, atherosclerosis, or liver cirrhosis progress. Researchers can uncover disease mechanisms and potentially identify biomarkers for early intervention or therapeutic targets using curated datasets.
Leverage spatial transcriptomics to map gene expression during tissue regeneration, driving advancements in wound healing and tissue engineering. Understanding these spatial gene dynamics can lead to improved therapeutic strategies.
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Leverage curated spatial transcriptomics data to gain comprehensive insights into tissue
architecture, disease progression, and cell-to-cell interactions.
Data processing and scientific reporting of metabolomics data are crucial but also tedious. Polly’s range of applications helped our team in accelerating these processes significantly and effectively.
Extremely impressed by Polly and its power to digest and integrate large datasets. The unique partnership that we have allows us to work hand-in-hand with software engineers and scientists to rapidly develop novel hypotheses that we can test in the lab.
We really were pleasantly surprised because this tool [El-MAVEN] is really useful in fast analysis of tracer data. It allows us to really look at a broad set of targets so you are not limited anymore for data analysis. A data analyst of mine stepped up to me and said, "I can do now in 3 hours what used to take 4 days.
Data analysis is the biggest bottleneck for metabolomics research. Elucidata has worked directly with us to streamline our analysis pipeline, accelerating the pace of our research.
We were looking for public data on a tight deadline to make decisions on our back up targets for solid tumors. Elucidata was able to understand our relevance criteria, identify, and deliver high-quality harmonized data ahead of schedule. We are really happy with their deliveries and this partnership.
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Spatial transcriptomics data captures the gene expression of cells in their native tissue context, providing insights into how gene activity is spatially organized. This data is essential for understanding tissue architecture and disease mechanisms.
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To obtain spatial transcriptomics data, methods like spatially resolved RNA sequencing and microarray-based technologies are employed. These techniques map gene expression to specific locations within tissue samples, allowing for detailed spatial analysis of gene activity.
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While single-cell transcriptomics analyzes gene expression at the individual cell level, spatial transcriptomics preserves the spatial context of gene expression within tissue samples, giving insights into how gene activity is organized within tissues.
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Transcriptome data is used to understand gene expression patterns, study cellular responses to different conditions, identify biomarkers, and explore the molecular basis of diseases. It is essential for advancing research in genomics, drug development, and precision medicine.
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Spatial transcriptomics offers the advantage of mapping gene expression to tissue architecture, enabling researchers to study how genes function in their natural spatial context. This give detailed insights into disease mechanisms and tissue-specific functions.
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Transcriptomics focuses on the study of RNA molecules and gene expression, whereas genomics involves the study of the entire genome, including DNA sequences and their structure. Transcriptomics gives insights into gene activity, while genomics focuses on genetic variation and inheritance.
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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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