Does it support multi-dataset files?

Yes, it includes a read_multiple_data_sets function to handle FCS files containing more than one dataset in a single file.

Can I use FlowIO to create new FCS files?

Yes, FlowIO includes a create_fcs function to generate standard FCS 3.1 files from NumPy arrays and custom metadata.

What file versions does FlowIO support?

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

Does FlowIO perform compensation or gating?

FlowIO is a lightweight handler for reading and writing files. For advanced analysis like gating, compensation, or FlowJo support, it is recommended to use the FlowKit library as a companion.

How does it handle large files?

FlowIO offers memory-efficient reading options, such as only_text=True, which allows you to extract metadata without loading the entire event dataset.

FlowIO Flow Cytometry Handler

Name: FlowIO Flow Cytometry Handler
Author: pur3v4d3r

bypur3v4d3r

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Data Science & ML

Parses and generates Flow Cytometry Standard (FCS) files, converting biological event data into NumPy arrays and DataFrames for scientific analysis.

About

FlowIO provides a specialized interface for interacting with FCS (Flow Cytometry Standard) files version 2.0 through 3.1. It enables developers and data scientists to extract event data as high-performance NumPy arrays, retrieve detailed instrument metadata, and export processed results back into standardized FCS formats. Designed for high efficiency and minimal dependencies, this skill simplifies the complex task of handling cytometry data within scientific research, clinical pipelines, and bioinformatics workflows, bridging the gap between raw instrument output and modern Python data science tools.

Key Features

Supports FCS versions 2.0, 3.0, and 3.1 parsing
Extracts event data directly into NumPy arrays or pandas DataFrames
1 GitHub stars
Handles complex metadata parsing including TEXT and ANALYSIS segments
Enables creation and export of custom FCS files from synthetic or modified data
Provides memory-efficient metadata extraction without loading full datasets

Use Cases

Converting proprietary instrument output to CSV formats for batch analysis
Preprocessing raw flow cytometry data for machine learning models
Building automated data pipelines for biological sample verification and metadata indexing

About

Key Features

Supports FCS versions 2.0, 3.0, and 3.1 parsing
Extracts event data directly into NumPy arrays or pandas DataFrames
1 GitHub stars
Handles complex metadata parsing including TEXT and ANALYSIS segments
Enables creation and export of custom FCS files from synthetic or modified data
Provides memory-efficient metadata extraction without loading full datasets

Use Cases

Converting proprietary instrument output to CSV formats for batch analysis
Preprocessing raw flow cytometry data for machine learning models
Building automated data pipelines for biological sample verification and metadata indexing