INFOGRAPHIC
Validating Biomarkers and Targets at Scale:
Multi-Study, Multi-Cohort Evidence with
Pythiomics Global Database Analytics
Single-cell RNA-seq data analysis is powerful, but findings can vary across different cohorts.
This means that no single dataset captures the full diversity of patient biology; findings from one cohort may not hold true in another, especially in heterogeneous diseases like cancer. In addition, rare cell types often remain underexplored due to the limited number of studies available.
The question is: can we combine datasets to achieve the statistical power needed for robust discovery?
Pythia BioSciences introduces the Pythiomics Global Database Analytics (PGDA) — a solution that enables cross-study analyses powered by Pythiomics, our rich and standardized single-cell database. This feature allows target exploration within a large, unified pool of multiple datasets rather than being limited to a single study.
In this infographic, you'll learn:
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Main challenges of performing single-cell RNA-seq data analysis using individual datasets
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How PGDA helps you identify more universally relevant biomarkers and targets
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Clear and detailed explanation of our process, from data curation and cross-dataset data transformation to database-wide analytics and visualization
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Explore Pythiomics:
A multi-omics database centered on quality, structure, and tracebility.
Pythiomics is a multi-omics database developed and curated by Pythia Biosciences with an aim to create a single, united multi-omics database for scientists to explore. By combining state-of-the-art AI techniques for metadata harmonization and cell type prediction with meticulous manual curation and quality control, Pythiomics DB provides a standardized and reliable data resource for biopharmaceutical companies and research institutions to accelerate data analysis, data integration, and data-driven drug discovery.