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flowio

Parse FCS files into NumPy arrays with channel metadata and CSV export.

1|Updated Mar 4, 2026
Search Tags:#pandas#csv#numpy#data-extraction#data-processing#fcs#flow-cytometry
One-click install
npx skills add https://github.com/Hung-3008/agusta --skill flowio-hung-3008
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: flowio
Source: https://github.com/Hung-3008/agusta/tree/main/.agents/skills/flowio
Command: npx skills add https://github.com/Hung-3008/agusta --skill flowio-hung-3008

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

FlowIO provides lightweight tooling to read and write Flow Cytometry Standard (FCS) files, extract per-event data as NumPy arrays, access metadata and channel information, and export data to CSV or DataFrame formats for preprocessing pipelines.

Core Features & Use Cases

  • Read FCS metadata and event data from FCS v2.0/3.0/3.1 files and convert events to NumPy arrays.
  • Access channel information (PnN, PnS, PnR) and export to CSV or Pandas DataFrame for downstream analysis.
  • Write modified data back to FCS or create new FCS files from NumPy arrays.
  • Use Case: preprocess flow cytometry data across multiple samples by loading, filtering, and exporting results for analyses.

Quick Start

Load an FCS file and return its events as a NumPy array along with channel metadata.

Frequently Asked Questions about flowio

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I parse FCS flow cytometry files and extract event data into NumPy arrays?▼

Use FlowIO to parse Flow Cytometry Standard files directly into NumPy arrays. It reads event data alongside channel metadata from FCS v2.0 through v3.1 files, enabling immediate downstream processing.

Can I export flow cytometry data from FCS files to a Pandas DataFrame or CSV?▼

Yes, FlowIO supports exporting extracted FCS event data and channel information to Pandas DataFrames or CSV formats. This facilitates seamless integration into data preprocessing pipelines.

Does FlowIO support reading metadata from different FCS file versions?▼

FlowIO supports reading FCS metadata and event data across versions 2.0, 3.0, and 3.1. It accurately extracts channel details like PnN, PnS, and PnR for comprehensive analysis.

What Python version is required to process FCS files with FlowIO?▼

Processing FCS files with FlowIO requires Python 3.9 or higher. This environment ensures compatibility with its NumPy array extraction and metadata handling capabilities.

How do I write modified data back to a new FCS file?▼

FlowIO allows you to write modified event data back to FCS formats or create entirely new FCS files from NumPy arrays. This supports round-trip data processing for cytometry samples.

What is the best way to handle multi-dataset FCS files for preprocessing?▼

FlowIO provides preprocessing options to handle multi-dataset FCS files effectively. It extracts per-event data and metadata, allowing you to load, filter, and export multiple samples.

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