Multi-Dimensional Viewer (MDV)

Transform complex single-cell, spatial transcriptomics, proteomics, imaging, and multiomics datasets into intuitive biological insight.

Oxford-Developed Architecture:

A robust, open-source platform optimized for interactive multi-modal omics analysis and high-performance visualization.

AI-Driven Bioinformatics:

Optionally integrated with ChatMDV, an advanced large language model layer that translates natural language commands directly into reproducible code and interactive visual outputs.

Contact the MDV Team View Example Projects

Analyse Complex Omics Data

MDV helps researchers explore large, complex biological datasets including single-cell RNA-seq, spatial transcriptomics, proteomics, imaging, genomics and clinical metadata.

Reveal Biological Insight

Move beyond static figures. Interactively visualise millions of cells, observations and features to identify biological patterns, generate hypotheses and support publication-ready results.

Work With Oxford Experts

Use MDV as a platform or work with our team for bioinformatics support, data integration, custom visualisation, project dashboards and collaborative interpretation.

Built for Academic, Biotech and Pharma Research

Modern biomedical research generates complex multi-dimensional data that is difficult to interpret using static plots and fragmented workflows. MDV brings analysis, visualisation and collaboration into a single interactive environment, helping research teams move from raw or processed omics data to actionable biological insight faster.

Whether you are investigating cell states, tissue architecture, disease mechanisms, biomarkers, therapeutic targets or translational datasets, MDV can help you extract more value from your data.

Contact the MDV Team Services and Support FAQs

Multiome Analysis

General Image Management

Spatial Clustering

Spatial Network Analysis

Spatial Marker QC

Spatial Image Analysis

GigaScience · June 2026

ChatMDV

Natural-language bioinformatics analysis inside MDV — ask questions, generate plots, and explore single-cell data without writing code.

Read publication Try ChatMDV

Cite this work

If you utilize ChatMDV or its retrieval-augmented generation (RAG) architecture in your studies, please cite our peer-reviewed publication:

APA: Kiourlappou, M., Todd, P., Kong, Y., Hire, J., Mohammed, S. F. N. N., Agarwal, D., ... & Hughes, J. (2026). ChatMDV: Reducing Technical Barriers in Bioinformatics Analysis using Large Language Models. GigaScience, 15, giag073. https://doi.org/10.1093/gigascience/giag073

BibTeX:

@article{kiourlappou2026chatmdv,
  title={ChatMDV: Reducing Technical Barriers in Bioinformatics Analysis using Large Language Models},
  author={Kiourlappou, Maria and Todd, Peter and Kong, Yaxuan and Hire, Jayesh and Mohammed, Sibgathullah Furquan Nawab and Agarwal, Devika and Sergeant, Martin and Zohren, Stefan and Marsden, Brian and Hughes, Jim},
  journal={GigaScience},
  volume={15},
  pages={giag073},
  year={2026},
  publisher={Oxford University Press},
  doi={10.1093/gigascience/giag073}
}