NanoString GeoMx Spatial Transcriptomics Analysis

Rosalind's GeoMx Digital Spatial Profiler (DSP) Data Analysis is a secure, cloud-based platform purpose-built to help researchers extract the most precise and reliable insights from NanoString GeoMx spatial transcriptomics data. Recognised as the recommended and preferred solution for best-in-class GeoMx data analysis, Rosalind is designed for pharma, biotech, and academic researchers at every skill level—requiring no programming or bioinformatics expertise to operate.

Unlike the embedded web server bundled with the GeoMx instrument, Rosalind is a true cloud platform that removes instrument constraints, eliminates scalability bottlenecks, and frees scientists to scan and analyse simultaneously. By combining intuitive guided workflows, AI-driven insights, and advanced visualisation tools, Rosalind transforms complex spatial data into actionable biological discoveries.

Core Platform Capabilities

• Cloud-based accessibility: Analyse data securely from anywhere in the world without tying up instrument computational resources or slowing down scanning performance.
• No-code workflows: Scientists at every skill level can run full analyses without programming knowledge in R, Python, or any other language.
• Real-time visualisation and filtering: See gene and region filtering updates instantly with complete transparency, minimising noise while preserving low expressors for clearer insights.
• Backlog data processing: Efficiently process accumulated datasets and uncover insights faster than instrument-bound pipelines allow.

Quality Control and Data Integrity

• Comprehensive QC analysis provides clear pass/fail status for every region of interest (ROI) and area of interest (AOI).
• Intuitive visualisations pinpoint exactly where regions or areas of interest fail during processing, enabling swift identification of issues.
• Adjustable QC parameters allow researchers to reprocess data instantly, ensuring each experiment yields the highest quality results.
• Gene lists can be defined before finalising an experiment, allowing key targets to be tracked through QC and retained in the dataset for downstream differential expression analysis.

AI-Powered and Knowledge Graph Features

• The Rosalind Knowledge Graph delivers generative AI-powered Literature Reviews in seconds for target genes of interest.
• Seamlessly explore correlations across all available GeoMx data from public repositories to reveal hidden biological connections.
• AI-driven insights are rooted in biological truth, transforming complex datasets into groundbreaking research outcomes.

Meta-Analysis and Multi-Omics Integration

• Integrate GeoMx spatial results with diverse multi-omics data layers using Rosalind's Meta-Analysis capability.
• Compare GeoMx datasets with precision or weave them into a broader multi-omics tapestry using Rosalind Intelligence to discover and interpret patterns in expression and regulation.
• Ensures GeoMx insights are contextualised within complex biological systems, providing a holistic understanding and a data mining foundation for future research.

Collaboration and Sharing

• Instantly share findings with collaborators anywhere in the world, accelerating discoveries and eliminating research bottlenecks.
• Designed to support seamless teamwork across geographies without compromising data security.

Expert Support

• Dedicated GeoMx expert team with PhD-level Customer Success specialists provides deep technical and scientific guidance.
• Support combines technical precision with real-world scientific understanding to help researchers extract the most meaningful insights from their data.
• Goes beyond standard support to ensure researchers stay ahead with cutting-edge analysis and actionable results.

Rosalind is available to pharma, biotech, and academic researchers through Enterprise, Professional, and Academic subscription tiers, as well as specialised licences for Service Providers, Contract Research Organisations (CROs), and Academic Core Labs. A GeoMx Masterclass Series is also available to help researchers master spatial biology data analysis efficiently and accurately.