Does it handle files with multiple datasets?

Yes, the skill includes specialized workflows for detecting and iterating through multi-dataset FCS files using the nextdata_offset parameter.

What file versions does FlowIO support?

FlowIO supports Flow Cytometry Standard (FCS) versions 2.0, 3.0, and 3.1.

Does FlowIO perform gating or compensation?

FlowIO is focused on file I/O and metadata extraction. For advanced analysis like gating or compensation, it is recommended to use the FlowKit library as a companion.

Can I convert FCS files to CSV using this skill?

Yes, FlowIO can extract event data into NumPy arrays or Pandas DataFrames, which can then be exported to CSV format easily.

What are the core dependencies for FlowIO?

FlowIO is a lightweight library that primarily requires Python 3.9+ and NumPy for data handling.

FlowIO FCS File Handler

Name: FlowIO FCS File Handler
Author: Activer007

byActiver007

Data Science & ML

Parses and manages Flow Cytometry Standard (FCS) files to extract event data as NumPy arrays and handle complex metadata.

About

FlowIO is a specialized skill designed for researchers and bioinformaticians working with flow cytometry data. It provides a lightweight Python-based interface to read, write, and manipulate FCS files (versions 2.0 through 3.1) with minimal dependencies. By enabling seamless conversion between raw FCS formats and standard data structures like NumPy arrays or Pandas DataFrames, it simplifies data preprocessing pipelines, metadata extraction, and instrument-specific data validation for scientific workflows.

Key Features

Full support for FCS versions 2.0, 3.0, and 3.1
0 GitHub stars
Comprehensive metadata and channel information parsing from TEXT segments
Extract event data directly into NumPy arrays for scientific analysis
Capability to create and export new FCS files with custom data and metadata
Advanced handling for multi-dataset files and problematic offset discrepancies

Use Cases

Validating instrument metadata and channel labels across large experimental datasets
Batch converting proprietary FCS files into CSV or DataFrame formats for downstream analysis
Preprocessing raw flow cytometry data for machine learning and statistical models

About

Key Features

Full support for FCS versions 2.0, 3.0, and 3.1
0 GitHub stars
Comprehensive metadata and channel information parsing from TEXT segments
Extract event data directly into NumPy arrays for scientific analysis
Capability to create and export new FCS files with custom data and metadata
Advanced handling for multi-dataset files and problematic offset discrepancies

Use Cases

Validating instrument metadata and channel labels across large experimental datasets
Batch converting proprietary FCS files into CSV or DataFrame formats for downstream analysis
Preprocessing raw flow cytometry data for machine learning and statistical models