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flowio

Parse FCS files and export events to NumPy arrays or DataFrames.

94|11|Updated Mar 26, 2026
Search Tags:#metadata#data-analysis#csv#numpy#parse#fcs#flow-cytometry
One-click install
npx skills add https://github.com/swaruplab/operon --skill flowio-swaruplab
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: flowio
Source: https://github.com/swaruplab/operon/tree/main/src-tauri/protocols/flowio
Command: npx skills add https://github.com/swaruplab/operon --skill flowio-swaruplab

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Parse and preprocess Flow Cytometry Standard (FCS) files to access events, channels, and metadata in Python.

Core Features & Use Cases

  • Read FCS files (versions 2.0/3.0/3.1) and expose event data as NumPy arrays
  • Access TEXT metadata and channel information (PnN, PnS, PnR) and classify channels (scatter/fluoro/time)
  • Support multi-dataset files with read_multiple_data_sets and nextdata_offset handling
  • Convert data to CSV or Pandas DataFrames, or work with raw arrays for pipelines
  • Handle preprocessing options (gain, log, time scaling) and robust error handling

Quick Start

Load an FCS file and export its events to a NumPy array or DataFrame.

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 in Python?▼

Parse FCS flow cytometry files in Python by reading version 2.0, 3.0, or 3.1 data to extract events and metadata. The tool exposes event data directly as NumPy arrays and supports exporting to Pandas DataFrames or CSV files.

What is the best way to extract metadata and channel information from an FCS file?▼

Extract FCS file metadata by accessing the TEXT segment to retrieve channel information like PnN, PnS, and PnR. You can classify channels into scatter, fluorescence, or time categories to prepare data for downstream analysis.

How do I handle multi-dataset FCS files during preprocessing?▼

Handle multi-dataset FCS files by using read_multiple_data_sets and nextdata_offset features. This allows sequential reading of multiple data segments within a single file while managing offset issues and ensuring robust error handling.

Can I export flow cytometry event data to NumPy arrays or CSV files?▼

Export flow cytometry event data to NumPy arrays or CSV files directly after parsing. You can also convert the extracted events into Pandas DataFrames for integration into Python-based data analysis pipelines.

Does FCS parsing support preprocessing options like gain and log scaling?▼

FCS parsing supports preprocessing options including gain adjustment, log transformation, and time scaling. These features allow you to normalize raw event data during extraction before exporting to arrays or DataFrames.

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