alterlab-flowio

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, transformation, gating, FlowJo 10 workspace import), recommend the FlowKit library (1.3.2 as of 2026-09), which is built on FlowIO by the same author.

Does NOT Trigger

Scenario	Use Instead
Compensation, transforms, gating, or FlowJo/GatingML workspaces	FlowKit (companion library, not a skill in this suite)
Clustering / dimensionality reduction of cytometry events	alterlab-scanpy, alterlab-umap
Statistical comparison of populations across samples	alterlab-statistical-analysis
Mass-cytometry or imaging-based spatial single-cell data	alterlab-squidpy-spatial
Registering and versioning the FCS files themselves	alterlab-lamindb

Installation

uv pip install flowio     # 1.4.0 as of 2026-09

Requires Python 3.9 or later; NumPy is the only runtime dependency.

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.

Part of the AlterLab Academic Skills suite.