Cytometry on the platform

Turn single-cell cytometry data into discovered clusters and spatial biology — FlowSOM clustering of flow and mass cytometry data, and imaging mass cytometry segmentation and neighborhood analysis. Reach for these tools when you have flow, mass, or imaging cytometry data and want reproducible, data-driven analysis. They sit in the experimental, cell-scale analysis arc rather than the small-molecule chemistry pipeline. See the Cell & Tissue overview for where this fits.

Cytometry overview

The Cytometry toolkit provides three tools: FlowSOM clustering for flow and mass cytometry data, and two imaging mass cytometry tools for cell segmentation and spatial biology. Each tool can be run independently or chained for end-to-end analysis.

Architecture

User Data (FCS files, TIFF/MCD images, CSV exports)
  --> Intent Detection
       |- Population discovery --> flowsom
       |    (SOM + consensus metaclustering)
       |- Cell segmentation --> imc_segment
       |    (nuclear/membrane segmentation, cell mask generation)
       +- Spatial analysis --> imc_spatial
            (neighborhood analysis, interaction scores, spatial statistics)
  --> Serverless Compute
  --> Structured Results with Visualizations

Tool inventory

Flow and mass cytometry tools

Imaging mass cytometry tools

Capabilities

FlowSOM clustering

The flowsom tool trains a self-organizing map over a flow or mass cytometry FCS file and consensus-clusters the SOM nodes into metaclusters. It returns per-event cluster labels, per-node and per-metacluster marker median intensities, metacluster summary statistics, and an optional star plot of the SOM grid. It is the only hosted FCS clustering tool: it does not run PhenoGraph, PARC or Leiden, and it does not gate or test differential abundance.

IMC cell segmentation

The imc_segment tool performs cell instance segmentation on multi-channel imaging mass cytometry images. It uses deep learning models trained on nuclear markers (e.g., Ir191, Ir193, histone H3) and membrane markers (e.g., CD45, Na-K-ATPase) to identify individual cells. Output includes cell masks, per-cell marker intensities, and cell morphology features (area, eccentricity, solidity).

Segmentation methods:

IMC spatial analysis

The imc_spatial tool analyzes the spatial organization of cells in tissue sections. It requires segmented cell data with coordinates and cell type labels. Analyses include:

When to use each tool

When to use other tools instead

Licensing

Citations

Van Gassen S, et al. FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data. Cytometry A. 2015;87(7):636-645. doi: 10.1002/cyto.a.22625

Greenwald NF, et al. Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning. Nat Biotechnol. 2022;40(4):555-565. doi: 10.1038/s41587-021-01094-0

Stringer C, et al. Cellpose: a generalist algorithm for cellular segmentation. Nat Methods. 2021;18(1):100-106. doi: 10.1038/s41592-020-01018-x

Chat Usage

Example Prompts

• "Cluster my flow cytometry panel with FlowSOM and summarize each metacluster"
• "Segment cells in my IMC image using Cellpose with nuclear marker Ir193"
• "Analyze spatial interactions between CD8 T cells and tumor cells in my tissue"
• "Run FlowSOM with 15 metaclusters on my mass cytometry data"

Workflow Chaining

• Feeds into: Differential gene expression (cell markers), Literature search (population biology), Genomics tools (marker gene annotation), Target Passport (immune cell targets)
• Accepts from: Data upload (FCS files, TIFF/MCD images), Sample metadata (group labels, clinical data), Marker panels (antibody-channel mapping), Genomics results (marker gene selection)

FAQ

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