Flowmeris

Flow cytometry analysis that runs entirely in the browser. You load a folder of FCS files as a group, gate them with one shared hierarchy (with per-sample adjustments), plot them, and export statistics, plots and Gating-ML. Every method is documented, and every result is checked against reference implementations.

• Input: FCS 2.0 / 3.0 / 3.1 / 3.2, including multi-dataset files. Compensation comes from each sample's $SPILLOVER keyword, an edited matrix, or an imported CSV.
• Scales: linear, log10, logicle, arcsinh and hyperlog, using the Gating-ML 2.0 definitions and editable parameters.
• Plots: dot, pseudocolor, density, contour (equal-probability or logarithmic levels, with outliers) and histogram; tiled per-sample panels and ridge plots for groups.
• Gates: rectangle, ellipse, polygon, quadrant (with offset legs), curly quadrant, spider, histogram range and bisector. Gates are hierarchical, you can drill into any population, and edits apply to the whole group or to one sample only (overrides are flagged everywhere).
• Output: tidy and wide statistics CSV with provenance, Gating-ML 2.0, gated events as FCS 3.1 or CSV, SVG/PNG plots at 300 dpi, and the workspace as JSON.
• Privacy: no server. Data stay in the browser's private storage. A Content-Security-Policy forbids network requests to anywhere but the app's own origin.

Validation

Check	Result
ISAC Gating-ML 2.0 compliance suite (51 reference gates)	exact event-level agreement
FCS parsing, compensation and transforms vs FlowKit 1.2.3 (pinned)	≤ 10⁻¹⁴, 10⁻⁹ and 10⁻¹⁰ relative
Statistics vs NumPy, on gated populations in linear and transformed units	≤ 10⁻¹² relative (transformed: 10⁻¹⁰); percentiles exact on linear channels
Replicate summaries and t-based 95% CI vs SciPy/pandas	≤ 10⁻¹² (t distribution 10⁻¹⁰)
Gating-ML exported by the app, evaluated by FlowKit (every gate kind)	identical membership, event by event
Gates drawn in the app, exported as Gating-ML and evaluated by FlowKit	identical population counts

Details: docs/validation. Methods: docs/methods.

Development

Requires Node ≥ 20 (pnpm via corepack). To regenerate the golden fixtures you also need uv.

corepack pnpm install

corepack pnpm fixtures:fetch   # large test files, SHA-256 verified

corepack pnpm dev              # docs at http://localhost:5173, app at /app/

corepack pnpm check            # lint + typecheck + unit/golden tests

corepack pnpm build && corepack pnpm e2e   # Playwright (Chromium, Firefox, WebKit)

corepack pnpm golden           # regenerate fixtures/golden from FlowKit (pinned in uv.lock)

corepack pnpm build:site && corepack pnpm preview   # docs + app (at /app/), as deployed

Layout

Notes for coding agents (layering, tests, conventions) are in CLAUDE.md.

Path	Contents
packages/model	workspace schema (zod), canonical JSON, fingerprints
packages/fcs	FCS parser and writer
packages/transforms, compensation, gating, stats, density, render	numerical kernels
packages/gatingml, export	Gating-ML reader/evaluator; CSV and Gating-ML export
packages/table	statistics table: derived columns, formulas, replicate summaries, table import
packages/testkit	test-only helpers: fixture paths, tolerances, synthetic data
packages/engine, storage	cached analysis pipeline; OPFS persistence
apps/web	React app and compute workers
docs	VitePress site: guide, methods, validation, decision records
fixtures, tools/golden	reference data and the golden-value generator (fixtures/golden/inputs: files the app writes, evaluated by FlowKit)

Status

Milestones M0–M3 of the implementation plan are complete, along with parts of M4–M5 (tiles, ridge plots, spider gates, Gating-ML/FCS/SVG/PNG export). Not yet implemented:

• XLSX export;
• PDF plot export;
• N×N plot matrix;
• Time-channel QC view;
• Boolean gates in the UI;
• compensation from single-stain controls;
• Gating-ML import in the UI;
• WebAssembly kernels.

License

Not yet chosen. Test fixtures keep their upstream licenses (see fixtures/PROVENANCE.md).