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flowio

Parse FCS 2.0–3.1 files to extract event data and metadata.

Updated May 24, 2026
Search Tags:#metadata extraction#data preprocessing#flow cytometry#python library#csv export#numpy arrays#fcs parsing
One-click install
npx skills add https://github.com/Estrella-231/Mathematical_modeling_tongmeng --skill flowio-estrella-231
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: flowio
Source: https://github.com/Estrella-231/Mathematical_modeling_tongmeng/tree/main/.agents/skills/flowio
Command: npx skills add https://github.com/Estrella-231/Mathematical_modeling_tongmeng --skill flowio-estrella-231

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

FlowIO removes the friction of manually inspecting and converting Flow Cytometry Standard (FCS) files by automatically parsing FCS headers/text metadata and extracting event data into analysis-ready structures.

Core Features & Use Cases

  • FCS metadata & channel parsing: Reads FCS version, event counts, channel labels (PnN/PnS), ranges (PnR), and other TEXT/ANALYSIS keywords for downstream processing.
  • Event extraction to NumPy / DataFrame workflows: Converts event DATA into a (events × channels) NumPy array (preprocessed or raw) and supports exporting/reshaping for tabular analysis.
  • FCS generation & re-export: Creates new FCS files from event arrays while preserving or updating TEXT metadata, enabling preprocessing pipelines and dataset transformations.
  • Real-world use case: You receive multiple FCS acquisitions from a core facility; use FlowIO to standardize channel naming, extract events into arrays, and export a CSV per file for modeling or QC.

Quick Start

Use the flowio skill to parse the uploaded FCS file, extract events into a NumPy array, and export them to CSV.

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 into NumPy arrays?▼

Flow cytometry FCS parsing extracts event data into a NumPy array (events × channels) alongside rich TEXT/ANALYSIS metadata. It supports FCS 2.0–3.1 versions, capturing channel labels, ranges, and event counts for downstream analysis.

Can I export FCS file metadata and event data to CSV for preprocessing?▼

Yes, FCS event data and metadata can be converted into tabular formats and exported to CSV. This standardizes channel naming and prepares flow cytometry datasets for modeling or quality control pipelines.

Does FCS parsing support both preprocessed and raw event extraction?▼

Yes, FCS parsing supports both preprocessed and raw event extraction. You can select either mode to pull event data directly into NumPy arrays, accommodating various flow cytometry analysis requirements.

How do I handle multi-dataset FCS files and offsets during flow cytometry data preprocessing?▼

When preprocessing multi-dataset FCS files, robust offset options allow accurate extraction of event data and metadata. This ensures correct alignment and reading of events across complex flow cytometry file structures.

What is the best way to generate new FCS files from modified event arrays?▼

The best way to generate new FCS files is by creating them directly from modified NumPy event arrays while preserving or updating TEXT metadata. This enables seamless preprocessing pipelines and flow cytometry dataset transformations.

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