Flow Cytometry

Tutorials for the omicverse.flow module — flow, spectral and mass cytometry.

New to ov.flow? Start with the case study — one complete analysis on real published data, in short ov.* calls with the reasoning in the text.

An event is a cell, but nothing else transfers from single-cell RNA-seq. The file format is FCS; the value a detector reports is contaminated by every other fluorochrome in the panel; the axis you look at is a nonlinear display scale; and the analysis is a sequential gating hierarchy rather than a clustering. ov.flow owns those, and ov.io.read_fcs owns the reading.

The rule the module is built around: a gate carries the display scale its boundary was drawn on. The same vertices mean different populations on a linear and a logicle axis, so a gate without its transform cannot be re-applied, saved or shared honestly. Saving to Gating-ML, running one strategy across a batch, and drawing the gate on the right axis all follow from that one decision.

Layer	Functions
display transforms	Logicle, Hyperlog, Asinh, Log, Linear, make_transform
gate geometry	RectangleGate, PolygonGate, EllipsoidGate, QuadrantGate, BooleanGate
gating strategy	GatingStrategy, GatingResult
compensation	compensate, spillover_to_compensation, spillover_spreading_matrix
interchange (Gating-ML 2.0)	to_gatingml, from_gatingml, read_gatingml, write_gatingml
clustering	flowsom, SOM, som_metacluster
plots	biaxial, histogram, backgate, hierarchy, spillover_heatmap, flowsom_heatmap

Notebooks 01-04 write their own simulated FCS file — nine known populations, a real $SPILLOVER keyword and a noise floor that goes negative — so they run with no data of your own. Notebook 05 runs the same module on two real, published, CC-BY-4.0 experiments downloaded from their original repositories.

• Reading FCS, compensation and display scales
• Gates, the gating strategy, and reading the numbers
• Saving a strategy and applying it to a batch (Gating-ML 2.0)
• FlowSOM — and what clustering does not replace
• The whole module on real data
• Case study — immunophenotyping PBMC after cardiac arrest

Installation

pip install omicverse

No extra needed. The transforms are derived from the Gating-ML 2.0 and Parks 2006 specifications and verified bit-for-bit against the reference C implementation, rather than wrapped from flowutils — whose numpy>=2 requirement would otherwise be imposed on every omicverse user. FlowSOM is pure numpy, with no R or Java dependency.