alterlab-flowio

name	alterlab-flowio
description	Parse and write FCS (Flow Cytometry Standard) files v2.0-3.1 with FlowIO — extract event data as NumPy arrays, read $-keyword metadata and channel/parameter definitions, and convert events to CSV or pandas DataFrame. Use when loading raw .fcs flow-cytometry files, inspecting channels and metadata, or preprocessing cytometry data for downstream gating and analysis. Part of the AlterLab Academic Skills suite.
license	MIT
allowed-tools	Read Write Edit Bash(python:*) Bash(uv:*)
compatibility	Self-contained — runs under `uv run python` with the skill's Python package installed; no API key or account required.
metadata	{"skill-author":"AlterLab","version":"1.0.0"}

FlowIO: Flow Cytometry Standard File Handler

Overview

FlowIO is a lightweight Python library for reading and writing Flow Cytometry Standard (FCS) files. Parse FCS metadata, extract event data, and create new FCS files with minimal dependencies. Supports FCS versions 2.0, 3.0, and 3.1 — ideal for backend services, data pipelines, and basic cytometry file operations.

When to Use This Skill

Use this skill when:

• FCS files require parsing or metadata extraction
• Flow cytometry data needs conversion to NumPy arrays
• Event data requires export to FCS format
• Multi-dataset FCS files need separation
• Channel information (scatter, fluorescence, time) must be extracted
• Cytometry files need validation or inspection
• Pre-processing is needed before advanced analysis

Related tool: For advanced analysis (compensation, gating, FlowJo/GatingML support), recommend the FlowKit library as a companion to FlowIO.

Installation

uv pip install flowio

Requires Python 3.9 or later.

Quick Start

from flowio import FlowData

# Read FCS file and inspect
flow = FlowData('experiment.fcs')
print(f"FCS Version: {flow.version}")
print(f"Events: {flow.event_count}")
print(f"Channels: {flow.pnn_labels}")

# Get event data as NumPy array, shape (events, channels)
events = flow.as_array()

import numpy as np
from flowio import create_fcs

# Write a new FCS file from a NumPy array.
# Gotcha: create_fcs takes a WRITABLE BINARY FILE HANDLE (not a path) and a
# FLATTENED 1-D event array — pass data.flatten(), not the 2-D matrix.
data = np.array([[100, 200, 50], [150, 180, 60]], dtype='float32')  # 2 events, 3 channels
with open('output.fcs', 'wb') as fh:
    create_fcs(fh, data.flatten(), ['FSC-A', 'SSC-A', 'FL1-A'])

Core Workflow

1. Read — Construct a FlowData('file.fcs') instance. Use only_text=True for metadata-only (memory-efficient) reads; pass offset/null-channel flags for problematic files.
2. Inspect — Read flow.version, flow.event_count, flow.pnn_labels, flow.pns_labels, channel-type indices, and the flow.text metadata dict.
3. Extract — Get a NumPy array via flow.as_array() (preprocessed) or flow.as_array(preprocess=False) (raw). Slice by channel type as needed.
4. Transform / export — Convert to a pandas DataFrame or CSV; or write a new FCS file with flow.write_fcs(path, ...) (takes a path) or create_fcs(fh, data.flatten(), ...) (takes a binary file handle + flattened events). Output is always FCS 3.1, single-precision float.
5. Multi-dataset — If a file holds multiple datasets, use read_multiple_data_sets() instead of the constructor.

Routing Guidance

• Need exact signatures, attributes, exceptions, or FCS keyword definitions? Read references/api_reference.md.
• Doing one of the core operations (read/parse, metadata, create, export, multi-dataset, preprocessing)? Read references/workflows.md for full code.
• Need a task recipe (inspect a file, batch a directory, FCS→CSV, filter events, extract channels)? Read references/recipes.md.
• Hitting an error, or want best practices / file-structure / troubleshooting? Read references/error-handling-and-troubleshooting.md.

References

• references/api_reference.md — Complete FlowData class, utility functions (read_multiple_data_sets, create_fcs), exception classes, FCS file structure, common TEXT-segment keywords, channel types, and example workflows.
• references/workflows.md — Full code for the core operations: reading/parsing, metadata & channel extraction, creating files, exporting/modifying, multi-dataset handling, and data preprocessing.
• references/recipes.md — Worked examples: inspecting contents, batch processing a directory, FCS→CSV conversion, event filtering & re-export, and channel extraction with statistics.
• references/error-handling-and-troubleshooting.md — Exception-handling patterns, best practices, FCS file-structure notes, a troubleshooting table, and integration notes (NumPy, pandas, FlowKit, web apps).

Summary

FlowIO provides essential FCS file handling for flow cytometry workflows — use it for parsing, metadata extraction, and file creation. For simple file operations and data extraction, FlowIO alone is sufficient; for complex analysis (compensation, gating), integrate with FlowKit or other specialized tools.