A voice-driven VR tool for exploring multichannel flow cytometry data on Meta Quest.

AI Assistant. Ask questions about your loaded dataset by speaking naturally. Query marker distributions, population statistics, and gating results. The AI Assistant is tuned for biomedical researchers — answering flow cytometry questions at a level appropriate for the domain. AI features improve automatically with no app update required.

3D visualization. Map any marker to the X, Y, or Z axis — scatter parameters, fluorescent markers, or computed dimensions. Switch axis assignments on the fly to explore different marker combinations. Visualize tSNE or UMAP dimensionality reduction results alongside original markers to explore connections between reduced-dimension clusters and the underlying measurements.

Multidimensional gating. Draw 3D gates to select cell populations. Apply multiple simultaneous gates, each with its own color. Switch axes with gates preserved — refine populations across different marker spaces. Export gated subsets as CSV for follow-up analysis in FlowJo or your own pipelines. For the full description of manipulating 3D scatter plots and gating, see the Gating and Scatter Plot Guide.

Data import. FlowGateVR loads compensated event CSV files, typically exported from FlowJo or similar cytometry analysis software. Side-load datasets via Meta Link, headset browser download, or SideQuest. Compatible with conventional and CyTOF panel data in CSV format. For a full description of the CSV format, including the exact FlowJo export settings, see the CSV Data Format Reference.

Collaboration. Multiple researchers share a VR session with voice communication and synchronized data. Co-navigate the same 3D environment in real time — useful for core facility walkthroughs, advisor-student sessions, and multi-site research discussions.

Need more performance? FlowGateVR runs natively on Meta Quest, but for very large cytometry datasets, a Windows desktop version is available with higher memory and GPU-accelerated rendering. Contact support@immsci.com for details.

This video shows an example immersive exploration where the 2D tSNE analysis results were added as 2 additional columns in the csv datafile with markers (columns) vs events (rows). You can then explore the connections between a reduced dimension cluster plot and the markers that were used to compute the reduction.