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flowio

Parse FCS files versions 2.0-3.1 into NumPy arrays and metadata.

8|Updated Nov 19, 2025
Search Tags:#bioinformatics#data parsing#numpy#data preprocessing#flow cytometry#fcs files
One-click install
npx skills add https://github.com/sanand0/scientific-research --skill flowio-sanand0
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: flowio
Source: https://github.com/sanand0/scientific-research/tree/main/.claude/skills/flowio
Command: npx skills add https://github.com/sanand0/scientific-research --skill flowio-sanand0

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires flowio, numpy, pandas, and includes references (resource) components.

What problem does it solve?

This Skill simplifies the complex task of reading and processing Flow Cytometry Standard (FCS) files, which are essential for analyzing cell data.

Core Features & Use Cases

  • FCS File Parsing: Reads FCS files (versions 2.0-3.1) to extract metadata and event data.
  • Data Conversion: Converts raw event data into NumPy arrays for further analysis.
  • Metadata Extraction: Retrieves channel information, acquisition dates, and instrument details.
  • Use Case: You have a directory of FCS files from a recent experiment and need to quickly extract the fluorescence channel data for each file to perform a preliminary analysis of cell populations.

Quick Start

Use the flowio skill to read the file 'experiment.fcs' and extract its event data as a NumPy array.

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 and extract flow cytometry event data in Python?▼

You can parse Flow Cytometry Standard (FCS) files by reading versions 2.0 through 3.1 to extract event data directly as NumPy arrays. This process also retrieves channel information and instrument metadata to prepare for cell population analysis.

Can I convert FCS flow cytometry data to a CSV or pandas DataFrame?▼

Yes, you can convert raw FCS event data into CSV or DataFrame formats for flow cytometry analysis. This data preprocessing capability transforms extracted NumPy arrays into structured tables for downstream analysis pipelines.

Does this FCS parsing approach support multi-dataset files from older flow cytometry instruments?▼

Yes, this FCS parsing approach supports file versions 2.0 through 3.1 and handles multi-dataset files. It accommodates data from older instruments while offering options for data preprocessing and error handling during extraction.

What metadata can I extract from an FCS file for flow cytometry analysis?▼

Extracting metadata from an FCS file retrieves channel information, acquisition dates, and instrument details. This metadata supplements the extracted NumPy array event data to provide full experimental context for flow cytometry analysis.

How do I handle errors when extracting fluorescence channel data from multiple FCS files?▼

When extracting fluorescence channel data from multiple FCS files, you can use built-in error handling options during the parsing process. This ensures data preprocessing continues reliably across a directory of experimental files without interruption.

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