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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Field notes, practical guides, and new thinking across single-cell analysis.
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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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AI Agents vs Workflows for Drug Discovery: Why We Chose One Task
Why we rebuilt CyteType as a structured 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 structured LLM workflow for more reproducible, auditable results in drug discovery pipelines.
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Multi-Omics Data for Target ID in Drug Discovery
How human genetics, single-cell transcriptomics, chromatin accessibility and proteomics combine into causal, cell-resolved target evidence, where integration usually fails, and how to grade the result before a programme is committed.
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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 existing annotation methods by 388% over GPTCellType, 268% over CellTypist, and 101% over SingleR in benchmarking.
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Why We Built AI Agents That Actually Understand Single Cell Data
The original 2025 announcement of CyteType for single-cell RNA-seq cluster annotation. CyteType has since been rebuilt as a structured AI workflow for accurate cell type identification, literature validation, and pathway-level reasoning beyond traditional marker-based methods. Built for researchers seeking precise, evidence-backed single-cell data analysis with comprehensive biological context.
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HVG selection
Learn how to select Highly Variable Genes (HVGs) in single-cell data, set corrected variance thresholds, and use custom blocklists to refine your analysis while ensuring meaningful cell heterogeneity.
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Spatial Transcriptomics and scRNA-seq: A Complementary Pair
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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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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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 Marker Gene Identification Workflow 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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A Practical Guide to Single-Cell RNA-Seq Cluster Annotation
How to annotate scRNA-seq clusters in 2026: marker genes, reference mapping, supervised tools, and separating cell identity from cell state.
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Best scRNA-seq Analysis Tools in 2026: Compared
Compare ScarfWeb, Loupe Browser, Trailmaker, Partek Flow, BBrowserX Pro, Omics Playground, Pluto Bio, and ROSALIND for scRNA-seq and multi-omics analysis in 2026.
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Building Cell Atlases: Mapping Biology Like Google Maps
Discover how building cell atlases parallels Google Maps - transforming scattered cellular data into integrated, navigable maps for drug discovery.
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Batch Effect Correction and Normalization in scRNA-Seq
Compare scRNA-seq normalization and batch correction methods, with current Seurat, Scanpy, Monocle 3, scran, Harmony, BBKNN and scVI workflows.
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Overcoming Bioinformatics Skill Gaps in Single-Cell Research
Discover solutions to bridge bioinformatics skill gaps in single-cell research, enabling easier scRNA-seq analysis for wet-lab scientists.
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What is Single Cell Transcriptomics?
Discover single-cell transcriptomics, a transformative technique for analyzing gene expression at the cellular level in biology and medicine.
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Navigating the Complexity of Single-Cell RNA-Seq Data Analysis
Explore key challenges and advanced strategies in scRNA-seq data analysis for both new and experienced researchers.
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