Harmonize in-house and public single-cell CITE-seq datasets to ML-ready formats. Accelerate your analysis and uncover deeper insights with custom-tailored solutions, optimized for single-cell data exploration and discovery.
Custom-curated datasets for your unique research objectives.
Expert QC & Analytics for RNA and surface protein modalities.
Multi-modal cell clustering solutions.
Elevate your data with expert metadata curation.
Integrate CITE-seq datasets from diverse public sources into a unified framework, precisely tailored to your research questions.
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Leverage our expert-assisted curation and prioritization of the most informative datasets from diverse public repositories.
Support for multiple input formats ensures no data is left behind—resulting in more cells, samples, and clarity.
Elucidata addresses the challenges posed by non-standardized pipelines and inconsistent data analysis practices.
Our expert-curated quality control practices ensure superior reliability and accuracy for both RNA and surface protein modalities. Our data pre-processing pipeline also handles sample demultiplexing, simplifying complex experimental designs for seamless analysis.
Our pipeline leverages both transcriptome and epitope data for cell clustering and cell type annotation.
Accelerate downstream analysis by harmonizing unstructured CITE-seq data through a configurable, transparent, and granular curation process tailored to your inclusion/exclusion criteria.
Leverage our flexible, transparent curation process that meticulously manages metadata at the dataset, sample, and feature levels using internationally recognized ontologies.
Receive Quality Verification Reports for each dataset, with over 50 QA/QC checks across both data modalities to ensure metadata completeness, accuracy, and full concordance.
Work with our experts to build customized, production-ready ML applications that cater to your research needs.
Leverage our expertise to construct tailored visualization apps and methods, unique to your research.
Use APIs or GUI to stream harmonized data on external tools like Spotfire, or your preferred analysis environment like react, Shiny, etc.
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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.
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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.
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Build multimodal classifiers to identify immune cell subsets and activation states that are not readily resolved using transcriptomics, and reveal novel cell type markers.
Harness harmonized CITE-seq and spatial datasets for a comprehensive view of cancer and immune cells in the tumor microenvironment. Reveal tumor-associated versus antagonistic immune populations and discover key localization and interaction patterns of immune subtypes.
Use CITE-seq data to analyze immune responses to therapeutic interventions versus disease progression, revealing critical cell subtypes, activation states, and epitope biomarkers.
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Unlock multi-omics insights to decode complex immune responses, disease mechanisms, and cellular heterogeneity
for robust drug discovery using CITE-seq datasets.
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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