flowkit

Files (4)

• SKILL.md
• compensation-and-gating.md · references/
• workspaces-and-results.md · references/
• analyze_gates.py · scripts/

SKILL.md (first lines)

# FlowKit

## When to use

Use FlowKit to apply or build cytometry gating strategies, analyze batches of
FCS samples, or reproduce supported FlowJo workspace analyses. It supports
GatingML 2.0 and a **subset of FlowJo 10 features**. Import success alone does
not establish agreement with FlowJo.

The examples and bundled helper target **FlowKit 1.3.2 on Python 3.13**.
Upstream supports additional Python versions; those were not exercised here.
The helper and examples were tested on synthetic FCS data, including a public
FlowJo 10.7.1 synthetic workspace fixture. They are not biological validation.

## Install

Use a separate environment; FlowKit 1.3.2 requires NumPy >2 and pandas <3:

```bash
uv venv --python 3.13 .venv-flowkit
uv pip install --python .venv-flowkit/bin/python "flowkit==1.3.2"
.venv-flowkit/bin/python -c "import flowkit; print(flowkit.__version__)"
```

The scientific package is BSD-3-Clause licensed; this skill is MIT licensed.

## Workflow

1. **Identify the analysis definition.** Use `Session` for a programmatic or
   GatingML strategy; use `Workspace` for FlowJo sample-specific gates,
   compensation, and transforms. Request the actual strategy or controls when
   biological thresholds have not been supplied.
2. **Inspect samples and channel identities.** Match detector/PnN labels to
   compensation matrices and gate dimensions; PnS marker names may be empty or
   repeated. Verify sample IDs: the default is FCS `$FIL`, which can differ
   from the current filename. Reject ID collisions before loading a batch.
3. **Establish the coordinate system.** Determine whether the supplied events
   are already compensated. Apply compensation before nonlinear transforms;
   match gate thresholds to the same transformed or untransformed coordinates.
   See [compensation and gating](references/compensation-and-gating.md).
4. **Check the hierarchy.** Preserve parent gates and full gate paths, including
   `root`. For a study, review acquisition/time stability, debris exclusion,
   singlets, viability, and phenotype gates as appropriate to its panel. Use
   single-stain controls for compensation and suitable negative/FMO controls
   for positivity; demonstration thresholds are not transferable biology.
5. **Analyze and inspect.** Run on all events, then check gate overlays and
   sample-level QC. A plot's subsample is not the population denominator.
   Review warnings and compare representative imported results to FlowJo.
6. **Export counts with denominators and provenance.** Keep gate paths,
   sample IDs, total event counts, input hashes, package versions, and the
   analysis definition. Keep biological replicates identifiable; events from
   one specimen are not independent experimental replicates.

## Apply an existing strategy

Set `FLOWKIT_SKILL_DIR` to this skill's installed directory. From the repository
root it is `skills/flowkit`. Paths below represent the user's local inputs.

```bash
FLOWKIT_SKILL_DIR="skills/flowkit"
uv run --no-project --python 3.13 --with "flowkit==1.3.2" \
  python "$FLOWKIT_SKILL_DIR/scripts/analyze_gates.py" \
  --gatingml gates.xml --fcs sample.fcs --output-dir results-gatingml
```

For a FlowJo workspace, supply **every FCS file in the selected group**:

```bash
uv run --no-project --python 3.13 --with "flowkit==1.3.2" \
  python "$FLOWKIT_SKILL_DIR/scripts/analyze_gates.py" \
  --workspace study.wsp --group "Study" \
  --fcs sample-a.fcs sample-b.fcs --output-dir results-workspace
```

The helper writes `gate_report.csv` and `provenance.json` to a new directory.
Each row includes `sample_event_count`, `parent_event_count`, and a full
`population_path`; empty-parent percentages are blank and flagged with
`relative_percent_defined=False`.
It rejects duplicate sample IDs, missing/extra workspace-group samples,
zero-event samples, and strategies without gates. It uses explicit input files,
does not follow paths embedded in the workspace, and runs without
multiprocessing or transformed-event caching. It still loads each sample into
memory; use manageable batches via the Python API for large studies.

`--filename-as-id` deliberately switches from `$FIL` to file basenames. Use it
only when those names match the analysis definition. See
[workspace analysis](references/workspaces-and-results.md) for partial-group
analysis, result interpretation, and fluorescence summaries.

## Build a strategy in Python

This runnable example uses `sample.fcs` with `FSC-A`, `FL1-A`, and `FL2-A`.
The matrix, thresholds, and transform parameters are **synthetic teaching
values**. Replace them with the study's validated settings.

```python
import flowkit as fk
import numpy as np

sample = fk.Sample("sample.fcs")
strategy = fk.GatingStrategy()
strategy.add_comp_matrix(
    "spill", fk.Matrix(
        np.array([[1.0, 0.1], [0.2, 1.0]]), ["FL1-A", "FL2-A"],
        fluorochromes=["FITC", "PE"],
    )
)
logicle = fk.transforms.LogicleTransform(
    param_t=262144, param_w=0.5, param_m=4.5, param_a=0
)
strategy.add_transform("logicle", logicle)
strategy.add_gate(
    fk.gates.RectangleGate("Cells", [
        fk.Dimension("FSC-A", range_min=50, range_max=300)
    ]),
    gate_path=("root",),
)
thresholds = logicle.apply(np.array([50.0, 600.0]))
strategy.add_gate(

From K-Dense-AI/scientific-agent-skills, MIT.

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