What file versions does FlowIO support?

FlowIO supports Flow Cytometry Standard (FCS) versions 2.0, 3.0, and 3.1, covering the majority of modern and legacy instrument outputs.

How does it handle multi-dataset files?

FlowIO includes specific error handling and methods (read_multiple_data_sets) to detect and process FCS files containing more than one data segment.

Does it support preprocessing like log scaling?

Yes, the skill can automatically apply gain scaling and logarithmic transformations (PnE keywords) based on the file's metadata when extracting event data.

Can I create new FCS files using this skill?

Yes, FlowIO can generate new FCS 3.1 files from NumPy arrays, allowing you to export synthetic data or modified experimental results back into standard formats.

FlowIO Flow Cytometry Handler

Name: FlowIO Flow Cytometry Handler
Author: ricable

byricable

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Data Science & ML

Parses and manages Flow Cytometry Standard (FCS) files, converting biological data into NumPy arrays and dataframes for analysis.

About

FlowIO is a specialized Python utility designed to bridge the gap between flow cytometry hardware outputs and modern data science ecosystems. It enables developers and researchers to programmatically extract high-dimensional event data, metadata, and channel information from FCS versions 2.0 through 3.1. By offering seamless conversion to NumPy arrays and Pandas DataFrames, it simplifies the creation of preprocessing pipelines, validation scripts, and automated batch analysis workflows for scientific research and bioinformatics applications.

Key Features

Extraction of event data as NumPy arrays or Pandas DataFrames
1 GitHub stars
Full support for FCS versions 2.0, 3.0, and 3.1
Comprehensive metadata and channel keyword parsing
Capabilities to create or modify FCS files from raw arrays
Advanced error handling for problematic or multi-dataset files

Use Cases

Building automated preprocessing pipelines for large-scale cytometry datasets
Extracting instrument-specific metadata and channel scaling for research auditing
Converting proprietary FCS files into CSV or DataFrames for machine learning models

About

Key Features

Extraction of event data as NumPy arrays or Pandas DataFrames
1 GitHub stars
Full support for FCS versions 2.0, 3.0, and 3.1
Comprehensive metadata and channel keyword parsing
Capabilities to create or modify FCS files from raw arrays
Advanced error handling for problematic or multi-dataset files

Use Cases

Building automated preprocessing pipelines for large-scale cytometry datasets
Extracting instrument-specific metadata and channel scaling for research auditing
Converting proprietary FCS files into CSV or DataFrames for machine learning models