Learning Center – Single-Cell Analysis
Single-cell RNA-seq analysis turns raw count data into cell identity and biological mechanism. This Learning Center track covers single-cell workflows, from cell-type annotation to cell-cell communication mapping, using guided methods your team can validate and reproduce. Explore the articles below to see how iSCanGuide™ takes single-cell data from raw matrix to mechanistic insight, in minutes, not weeks.
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 [...]
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 [...]
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 [...]
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 [...]