A curated index of articles, updates, events, and published work across single-cell research.
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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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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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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An updated guide to the most useful public single-cell RNA-seq databases in 2026, including archives, atlas portals, and domain-specific resources for data discovery and reuse.
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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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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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Discover how spatial transcriptomics and single-cell RNA-seq complement each other to drive next-generation biological insights. Learn about emerging platforms, multi-omics integration, and how Nygen Analytics empowers researchers to leverage both technologies.
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Discover how integrating spatial transcriptomics with scRNA-seq data enhances biological insights by mapping gene expression to tissue architecture. Learn key integration methods and real-world applications.
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Discover when single-cell RNA sequencing is sufficient for your research needs. This guide explains ideal use cases, analysis techniques, and practical implementations for leveraging scRNA-seq data effectively.
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Learn how to identify marker genes in single-cell RNA-seq data without coding using Nygen's intuitive platform. This step-by-step guide covers data upload, quality control, clustering, marker detection, and dynamic analysis with detailed screenshots and expert tips.
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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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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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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 scRNA-seq cluster annotation in 2026, covering marker gene inspection, reference atlas mapping, supervised classification tools, and strategies for resolving ambiguous or disease-associated populations.
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Uncover the top single-cell RNA sequencing (scRNA-seq) and multi-omics data analytics tools of 2025. This comprehensive guide reviews 8 platforms to facilitate data analysis and reveal cellular insights.
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Discover how building cell atlases parallels Google Maps - transforming scattered cellular data into integrated, navigable maps for drug discovery.
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A practical guide to batch effect correction and normalization in scRNA-seq data, covering when integration is justified, leading tools including Harmony, Seurat, BBKNN, and scVI, evaluation strategies, and how to avoid overcorrection.
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Discover solutions to bridge bioinformatics skill gaps in single-cell research, enabling easier scRNA-seq analysis for wet-lab scientists.
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Discover single-cell transcriptomics, a transformative technique for analyzing gene expression at the cellular level in biology and medicine.
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Explore key challenges and advanced strategies in scRNA-seq data analysis for both new and experienced researchers.
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