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RamanEbrahimi
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flowio

Parse Flow Cytometry Standard (FCS) files into NumPy arrays and Pandas DataFrames.

3|Updated Apr 17, 2026
Search Tags:#data parsing#flow cytometry#python library#fcs files#cytometry analysis
One-click install
npx skills add https://github.com/RamanEbrahimi/raman-marketplace --skill flowio-ramanebrahimi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: flowio
Source: https://github.com/RamanEbrahimi/raman-marketplace/tree/main/plugins/agentic-research/skills/scientific-skills/flowio
Command: npx skills add https://github.com/RamanEbrahimi/raman-marketplace --skill flowio-ramanebrahimi

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

FlowIO solves the challenge of manually parsing complex Flow Cytometry Standard (FCS) files by providing an efficient and accurate tool for reading, processing, and manipulating these files.

Core Features & Use Cases

  • Automated FCS File Reading: Automatically reads and parses FCS files from various sources, handling version 2.0 to 3.1.
  • Event Data Conversion: Converts FCS data to NumPy arrays and Pandas DataFrames for analysis and visualization.
  • Metadata Extraction: Extracts rich metadata such as event count, channel information, and analysis data from FCS files.
  • Data Preprocessing: Offers preprocessing steps including gain scaling, logarithmic transformation, and time scaling for FCS data.
  • Multi-Dataset Handling: Handles multi-dataset FCS files with multiple datasets within a single file.
  • Use Case: FlowIO can be used to quickly analyze a batch of FCS files from flow cytometry experiments, automatically processing them into a usable format for further analysis or reporting.

Quick Start

Load an FCS file and print the version information with the following command:

flow_data = FlowData('example.fcs')
print(flow_data.version)

Frequently Asked Questions about flowio

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

FAQPage Schema
How do I parse FCS files for flow cytometry analysis in Python?▼

Parsing FCS files for flow cytometry analysis extracts raw event data and metadata, converting it into NumPy arrays and Pandas DataFrames. This automated process handles FCS versions 2.0 to 3.1, enabling efficient data extraction and manipulation for research workflows.

What is the best way to extract metadata from flow cytometry standard files?▼

Extracting metadata from flow cytometry standard files is done automatically during the parsing process, retrieving rich information like event counts and channel details. This provides immediate access to critical experimental parameters required for downstream analysis.

Can I convert FCS event data to a Pandas DataFrame for visualization?▼

Yes, you can convert FCS event data to a Pandas DataFrame for visualization and analysis. The parsing process natively transforms raw flow cytometry events into structured DataFrame formats, making the data immediately accessible for Python-based data visualization routines.

Does Python support preprocessing flow cytometry data with logarithmic transformation?▼

Python supports preprocessing flow cytometry data with logarithmic transformation, gain scaling, and time scaling during the parsing stage. These built-in preprocessing steps prepare raw FCS event data directly for accurate downstream analysis and reporting.

How do I handle multi-dataset FCS files when processing flow cytometry data?▼

Handling multi-dataset FCS files when processing flow cytometry data is fully supported, allowing you to manage multiple datasets contained within a single file. This capability ensures accurate event extraction and metadata interpretation across complex experimental outputs.

Are there limitations when parsing older FCS file versions for cytometry analysis?▼

Parsing older FCS file versions for cytometry analysis has no limitations within the supported range, as the parser accommodates versions 2.0 through 3.1. This ensures broad compatibility with legacy flow cytometry data without requiring manual format conversion.

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