Gating

Gating requires a license. Viewing does not.

Gate types

Type	Use it for
Rectangle	Quick, axis-aligned regions. The fastest way to a first gate.
Polygon	Irregular populations — most real lymphocyte gates. Click to place vertices, close the shape to finish.
Ellipse	Roughly elliptical populations, where a polygon would be busywork.
Quadrant	Four regions from one crosshair. The classic two-marker readout.
Range	An interval on a single axis — useful on histograms and for time-based gates.

Escape cancels a gate you are part-way through drawing.

Editing a gate

Select it, then drag the whole gate or its vertices and handles. Statistics recompute as you move it, so you can watch a frequency settle rather than guessing where the boundary belongs.

• Delete or Backspace removes the selected gate.
• Ctrl+Z undoes a gate change; Ctrl+Y (or Ctrl+Shift+Z) redoes it.

Undo and redo cover gate edits — draw, move, delete — which is what you need when a boundary was better two adjustments ago.

Hierarchy and drill-down

Double-click a gate to drill into it. The plot then shows only events inside that gate, and new gates become its children. Escape goes back up. The breadcrumb above the plot always names where you are.

This matters for interpretation, not just navigation: a child population’s frequency is a percentage of its parent. A tree that mixes denominators — some populations as a percentage of all events, some of their parent — is the single most common way a flow figure misleads. Drilling down is how you keep them consistent.

The conventional order is:

1. Scatter gate, to exclude debris
2. Singlets
3. Live cells, if you have a viability dye
4. Lineage gates below that

Colours

Each gate gets a colour, used consistently in the gate list, in plots, and in exports — so a population in a figure is identifiable without a legend lookup.

Automated gating

Two independent routes, and they solve different problems.

Clustering

Apply a clustering algorithm to the current population and turn the result into gates:

Algorithm	Character
K-means	Fast, wants a cluster count, assumes roughly round and similarly-sized clusters
DBSCAN	Finds clusters by density, handles odd shapes, does not need a count, and can label sparse events as noise
Gaussian Mixture Models	Probabilistic and elliptical; better than K-means for overlapping populations

Clustering knows nothing about immunology — it finds structure. Whether a cluster is a population is your call, and the result is a set of gates you can then edit by hand like any other.

AI-1 and the copilot

Different from clustering: these propose named populations.

• AI-1 identifies a published list of immune populations on your machine and abstains on the rest.
• The AI copilot plans a whole strategy from a plain-language request, and you approve it step by step.

A note on compensation and gate coordinates

If you change compensation after drawing gates, check the gates. A gate is a region in a coordinate space, and changing the transform under it changes which events fall inside. Compensate first, then gate, whenever you can.