Advaita Bio Launches iSCanGuide™: Unified Single-Cell and Spatial Transcriptomics Analysis, Powered by GPU-Accelerated Infrastructure
GPU-Accelerated Platform Unifies Fragmented Workflows, Turning Weeks of Analysis into Minutes
ANN ARBOR, Mich., May 7, 2026 /PRNewswire/ -- Advaita Bio today announced the launch of iSCanGuide, a cloud-based platform engineered to transform large-scale single-cell and spatial transcriptomics datasets into biologically meaningful results. Built on 20 years of validated science, the platform is integrated with Advaita's flagship platform, iPathwayGuide™, replacing fragmented workflows with a unified, GPU-powered discovery environment.
Single-cell and spatial transcriptomics generate complex datasets — but most researchers are still stitching together fragmented tools to analyze them, writing custom scripts, exporting between platforms, and losing weeks to manual workflows. iSCanGuide eliminates that bottleneck with a unified, GPU-accelerated environment that turns what used to take weeks into minutes, built on the same validated science that underpins Advaita's pathway analysis platform, trusted by over 13,000 researchers worldwide.
"Researchers are drowning in data and starving for insight," said Mike Mattacola, CEO of Advaita Bio. "iSCanGuide collapses what used to be a weeks-long manual process into minutes — from count matrices to annotated cell types, spatial context, and mechanistic pathway analysis in a single platform."
Key Features & Compatibility:
- Real-Time Interaction: GUI-driven, zero-code interface with multiple navigation modes — accessible enough for first-time users, powerful for expert analysis.
- Standardized Best-Practice Workflows: Ensure reproducibility and consistency across single-cell and spatial transcriptomics experiments with standardized, validated analysis pipelines.
- Single-Cell Transcriptomics Analysis: Natively supports .h5ad, .h5, 10x CellRanger, and ParseBio outputs, enabling interactive and iterative analysis from initial QC and filtering through clustering, annotation, and trajectory analysis, with flexible workflows that adapt to your data at every stage.
- Spatial Transcriptomics Analysis: Map gene expression in its native tissue context with full support for 10x Genomics Visium (Standard/HD) and Xenium. Visualize spatial cell distributions and identify spatially variable genes — all within the same environment used for single-cell RNA-seq, with no data export required.
- Integrated Frameworks: Feeds directly into iPathwayGuide™ proprietary Impact Analysis methodology for mechanistic pathway analysis at single-cell resolution — connecting cell type annotation to pathway-level biological interpretation in a single workflow.
- Cell-to-Cell Communication Analysis: Characterise intercellular signalling networks by identifying ligand-receptor interactions and communication patterns across cell types — revealing how cells coordinate behaviour within their native tissue environment.
"Single-cell and spatial analysis has been defined by fragmentation — too many tools, too many parameters, and no continuity between clustering and biological interpretation. The steep learning curve means critical know-how walks out the door every time an analyst leaves, and sharing results is harder than it should be," said Dr. Sorin Draghici, Founder and Chief Scientific Officer. "iSCanGuide replaces all of that with a single platform that takes researchers from count matrices to mechanistic pathway understanding — with sensible defaults, one-click sharing, and every setting captured. It connects directly to iPathwayGuide's proprietary Impact Analysis methodology, so the entire workflow runs in minutes rather than weeks."
To learn more about iSCanGuide, please visit our website.
About Advaita Bio
Advaita Bio builds AI-powered software for pathway analysis, variant interpretation, and single-cell analytics. Trusted by over 13,000 researchers and backed by nearly 20,000 peer-reviewed citations, the platform is built on two decades of scientifically validated algorithms to deliver biological insight that conventional tools miss.
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