flowio

Parse, analyze, and generate flow cytometry FCS files from NumPy arrays.

Updated May 10, 2026

Search Tags:#metadata extraction #data parsing #file creation #flow cytometry #fcs files #cytometry

Ready to run

npx skills add https://github.com/Imad-Oute/ResearchForge --skill flowio-imad-oute

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: flowio
Source: https://github.com/Imad-Oute/ResearchForge/tree/main/OpenSource-Projects/claude-scientific-skills/scientific-skills/flowio
Command: npx skills add https://github.com/Imad-Oute/ResearchForge --skill flowio-imad-oute

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

It enables users to parse, extract, and generate flow cytometry FCS files efficiently, facilitating automated data processing workflows.

Core Features & Use Cases

FCS File Parsing: Read and extract event data and metadata from various FCS versions (2.0-3.1).
Metadata & Channel Extraction: Retrieve detailed information about channels and experimental parameters.
FCS Data Creation: Generate new FCS files from NumPy arrays for data analysis and sharing setups.
Use Case: Use this skill to convert raw flow cytometry data into analyzable formats like CSV, or to prepare standardized FCS files for pipeline integration.

Quick Start

Load an FCS file, access its event data as a NumPy array, and export processed data into a new FCS file with custom metadata.

Frequently Asked Questions about flowio

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

FAQPage Schema

How do I parse flow cytometry FCS files to extract event data and metadata?▼

Flow cytometry FCS files can be parsed to extract event data and metadata by reading supported FCS versions 2.0 through 3.1. This process retrieves detailed channel information and experimental parameters, outputting event data as NumPy arrays for analysis.

Can I generate new FCS files from NumPy arrays for bioinformatics pipelines?▼

You can generate new FCS files directly from NumPy arrays to create standardized datasets for bioinformatics pipelines. This allows you to prepare custom flow cytometry data with specific metadata for seamless integration and data sharing setups.

Does this FCS file handler support multi-dataset files and older FCS versions?▼

This FCS file handler supports multi-dataset files and various FCS versions ranging from 2.0 to 3.1. It implements robust error handling to ensure accurate processing of complex flow cytometry datasets across different format specifications.

What is the best way to convert raw flow cytometry data into analyzable formats like CSV?▼

The best way to convert raw flow cytometry data into analyzable formats like CSV is by parsing the FCS file to access event data as a NumPy array. Once extracted, this structured data can be easily exported for downstream analysis.

How does metadata extraction from FCS files work for experimental parameters?▼

Metadata extraction works by reading the FCS file headers to retrieve detailed information about channels and experimental parameters. This allows you to access critical flow cytometry metadata needed for accurate data interpretation and pipeline integration.

Are there limitations when handling complex flow cytometry datasets with multiple datasets in one file?▼

There are no significant limitations for multi-dataset files, as the handler implements robust error handling specifically designed for complex flow cytometry datasets. It accurately processes multiple datasets within a single FCS file to support research workflows.