The biology in your ligand-receptor analysis, and how to read it
Ligand-receptor analysis is easy to run and easy to over-read. How to get cell-cell communication biology from the ranked table, with LIANA and ScarfWeb.
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A curated index of articles, updates, events, and published work across single-cell research.
Cherry-picking is a bigger annotation risk than hallucination
Cherry-picking in LLM cell annotation survives reference checks unlike hallucination. Learn why biased marker subsampling is the harder risk and how CyteType prevents it.
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Everyone Is Building AI Agents for Drug Discovery. We Built a Workflow for One Task. Here's Why.
Why we rebuilt CyteType as a deterministic AI workflow instead of an agent, and why that distinction matters for production cell annotation pipelines.
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What We Got Wrong About AI Agents for Cell Annotation
Running CyteType's AI agents across thousands of single-cell RNA-seq datasets in production exposed run-to-run variance, selective evidence gathering, and inconsistent depth. We rebuilt cell type annotation as a deterministic LLM workflow for reproducible, auditable results in drug discovery pipelines.
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Integrating Multi-Omics Data for Effective Target Identification in Drug Discovery
Discover how multi-omics integration is reshaping drug discovery by uncovering disease mechanisms, prioritizing drug targets, and connecting genomics, epigenomics, transcriptomics, proteomics, and metabolomics into a usable biological model.
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Public single-cell RNA-seq databases worth using in 2026
An updated guide to the most useful public single-cell RNA-seq databases in 2026, including archives, curated discovery portals, and domain-specific atlases for data discovery and reuse.
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CyteType: Evidence-Based Cell Annotation with Multi-Agent AI
CyteType uses a five-agent AI framework for accurate cell type annotation in scRNA-seq data. Outperforms reference-based methods by 300%+ in benchmarking.
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Why We Built AI Agents That Actually Understand Single Cell Data
CyteType is multi-agentic annotation system designed for single-cell RNA-seq cluster annotation. Designed to deploy three specialized AI agents to provide accurate cell type identification, literature validation, and pathway-level reasoning beyond traditional marker-based methods and beyond. Built for researchers seeking precise, evidence-backed single-cell data analysis with comprehensive biological context.
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Integrating Spatial Transcriptomics with Single-cell RNA-seq
How to integrate spatial transcriptomics with scRNA-seq: when spatial context helps, the key integration methods, and worked tissue examples.
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Spatial Transcriptomics and Single-cell RNA-seq: Complementary Technologies
The current state of spatial transcriptomics and scRNA-seq: single-cell-resolution platforms, foundation models for integration, spatial multi-omics, and 3D tissue atlases.
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When is Single-cell RNA-seq Enough, and When Do You Need Spatial Data?
When is scRNA-seq enough on its own? A guide to its use cases, the workflow from clustering to cell type annotation, and when to add spatial data.
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No-Code Workflow for Marker Gene Identification (Step-by-Step) in ScarfWeb
Find marker genes in scRNA-seq without code using ScarfWeb. Step-by-step walkthrough from upload and QC through clustering, marker detection, pseudotime, and export.
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Accelerate Marker Gene Detection in scRNA-seq: A No-Code Approach
Uncover the essentials of marker gene identification in single-cell RNA sequencing. This article covers no-code solutions, methodologies, and case studies across various fields.
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Designing Robust Single-Cell RNA-Seq Experiments: A Practical Guide
Learn the essentials of designing robust single-cell RNA-seq experiments with our practical guide for wet-lab scientists. Covers sample preparation, controls, sequencing parameters, and analysis approaches—including how no-code platforms eliminate computational barriers.
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AGI is Far, but ASI is Here
Explore how ASI transforms drug discovery by harnessing domain-specific models and advanced computing tools for faster, smarter biomedical innovation.
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