iSCanGuide™
Single-Cell vs. Bulk RNA-Seq: When Each Makes Sense
Key takeaways Bulk RNA-seq gives one averaged expression value per gene, which suits large cohorts, archived samples and treatment-response studies. Single-cell RNA-seq measures every cell, which suits cell atlases, rare populations, trajectories and cell–cell signaling. Deconvolution tools such as CIBERSORT, MCP-counter and EPIC estimate only the cell types already in their reference. When fresh, viable cells are not available, single-nucleus RNA-seq or bulk RNA-seq with deconvolution are the usual alternatives. Many strong studies combine both: single-cell discovery in a few samples, then bulk validation across a large cohort. Single-cell or bulk RNA-seq: one microphone or many? [...]
iSCanGuide v3.0: Analyze Tens of Millions of Cells, Interactively
Today, AdvaitaBio is launching iSCanGuide v3.0, a major new release designed to help researchers analyze single-cell and spatial transcriptomics data at a scale that has often been slow, fragmented, and difficult to manage. With support for projects spanning hundreds of samples and tens of millions of cells, iSCanGuide v3.0 brings interactive, GPU-accelerated analysis to workflows that used to demand significant time, infrastructure, and computational expertise. This launch is about more than performance alone. It changes what researchers can do in the moment. Instead of breaking analysis into disconnected steps across multiple tools, teams can move through a guided, end-to-end [...]
From Organoid to Original Tumor: How iSCanGuide™ Put Its Own Model to the Test
By Cristiana Iosef, D.V.M/Ph.D., Principal Life Scientist A glioblastoma (GBM) organoid grown in a dish is a bet. The bet is that a few millimeters of lab-grown tissue actually behaves like the tumor it is meant to stand in for, that its architecture, its signaling, its invasive edge all mirror what is happening inside a patient. Most of the time that bet is taken on faith. Here’s the finding up front: in a new re-analysis of data from the GLORIA glioblastoma clinical trial (Phase I/II, NCT04121455), Advaita’s team found that a GBM organoid’s spatial architecture and its NOTCH1–DLL3/4 signaling [...]
From Gene List to Cell Identity: A Continuous, Guided Workflow for Single-Cell RNA-Seq Analysis
iSCanGuide™ eliminates the bottleneck in single-cell analysis, rapidly converting raw sequencing output into cell identity. Key Takeaways Single-cell RNA-seq analysis lets researchers resolve individual cell types, states, and transitions, but the analytical workflow required to get there has become the real bottleneck. Most single-cell RNA-seq workflows are stitched together from separate programs, creating hand-off errors, untraceable pipelines, and open questions about which method is “correct.” Recent peer-reviewed work confirms that reproducibility and methodological inconsistency remain significant obstacles in scRNA-seq analysis.1,2 iSCanGuide™ unifies data QC, clustering, cell annotation, and additional analyses with pathway-level biology (powered by iPathwayGuide™) into [...]