Analyzes flow cytometry data with FlowKit, including spillover compensation, logicle and biexponential transforms, hierarchical gating, GatingML strategies, and supported FlowJo 10 workspaces. Use for
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Analyzes flow cytometry data with FlowKit, including spillover compensation, logicle and biexponential transforms, hierarchical gating, GatingML strategies, and supported FlowJo 10 workspaces. Use for reproducible gate counts, population percentages, gated fluorescence summaries, or reproducing a FlowJo analysis in Python. For FCS metadata inspection or file-format repair alone, use FlowIO.
name: flowkit
description: Analyzes flow cytometry data with FlowKit, including spillover compensation, logicle and biexponential transforms, hierarchical gating, GatingML strategies, and supported FlowJo 10 workspaces. Use for reproducible gate counts, population percentages, gated fluorescence summaries, or reproducing a FlowJo analysis in Python. For FCS metadata inspection or file-format repair alone, use FlowIO.
license: MIT
compatibility: Requires Python 3.13 with flowkit==1.3.2 for the tested environment. Dependencies include FlowIO, FlowUtils, NumPy, pandas, SciPy, lxml, and Bokeh. Installation needs network access; analysis uses local FCS/XML/WSP files without credentials. FlowUtils needs a C compiler if a compatible wheel is unavailable.
metadata:
version: "1.1"
skill-author: K-Dense Inc.
last-reviewed: "2026-09-30"
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:
The scientific package is BSD-3-Clause licensed; this skill is MIT licensed.
Workflow
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.
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.
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.
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.
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.
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.
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 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.
GatingML exports a template by default. When custom per-sample gates exist,
use session.export_gml(handle, sample_id=sample.id) for that sample's strategy.
A single template export does not preserve every sample-specific override.
Interpretation checks
count is the number of events passing the gate and its ancestors.
absolute_percent is percent of all sample events; relative_percent is
percent of the immediate parent. These are percentages, not fractions.
Gate names can repeat under different parents. In the helper's output use
(sample_id, population_path) as the identifier. Paths are JSON arrays
inside CSV cells. FlowKit's native report stores a quadrant's owner
separately in quadrant_parent; its gate_path alone omits that owner.
A zero-event parent makes a child percentage biologically undefined;
FlowKit 1.3.2 reports zero for ordinary children and NaN for quadrants.
The helper exports both as blank with an explicit false flag. This differs
from a defined 0% for an empty gate whose parent contains events.
Compensated negative fluorescence is legitimate. Do not clip it to zero or
discard those events merely to permit a logarithmic transform.
Define whether “MFI” means mean or median and name the event source.
A transformed display value is not an intensity on the original scale.
References
Compensation and gating: event
sources, detector order, transform semantics, gate paths, and plotting.
Workspaces and results: sample
matching, missing FCS files, FlowJo limits, and gated fluorescence summaries.