How does FlowIO handle data preprocessing?

By default, FlowIO automatically applies gain scaling and logarithmic transformations (PnE) to raw data during the parsing process, though these can be disabled if raw event values are required.

Is FlowIO suitable for large-scale data pipelines?

Absolutely. Its lightweight design, minimal dependencies, and native NumPy integration make it ideal for high-throughput bioinformatics pipelines and backend data services.

What FCS versions does FlowIO support?

FlowIO supports FCS versions 2.0, 3.0, and 3.1, covering the vast majority of modern and legacy flow cytometry data formats used in clinical and research settings.

Can I use FlowIO to create new FCS files from scratch?

Yes, FlowIO includes a create_fcs utility that allows you to generate valid FCS 3.1 files from NumPy arrays, custom channel names, and user-defined metadata.

Does it support files with multiple datasets?

Yes, the skill provides specific workflows for detecting and reading multiple datasets within a single FCS container using the dedicated read_multiple_data_sets function.

FlowIO FCS File Handler

Name: FlowIO FCS File Handler
Author: K-Dense-AI

byK-Dense-AI

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3,718

Data Science & ML

Parses and manages Flow Cytometry Standard (FCS) files, enabling seamless conversion to NumPy arrays and metadata extraction for bioinformatics workflows.

About

FlowIO is a lightweight Python utility designed for the high-performance handling of Flow Cytometry Standard (FCS) files versions 2.0 through 3.1. It empowers researchers and data scientists to programmatically extract event data into NumPy arrays, manage complex metadata segments, and generate new FCS files from scratch. Ideal for building automated data pipelines or preprocessing cytometry data for machine learning models, FlowIO handles technical nuances like gain scaling, logarithmic transformations, and multi-dataset file structures with minimal dependencies.

Key Features

High-speed conversion of event data to NumPy arrays for scientific analysis
Full support for FCS versions 2.0, 3.0, and 3.1 file parsing and creation
Integrated preprocessing for gain scaling and logarithmic transformations
Advanced metadata extraction from TEXT and ANALYSIS file segments
Robust support for multi-dataset FCS files and offset discrepancy correction
3,718 GitHub stars

Use Cases

Automating the preprocessing of large flow cytometry datasets for machine learning training
Converting proprietary instrument data into CSV or DataFrames for custom visualization
Validating and repairing corrupted FCS files with offset or header discrepancies

About

Key Features

High-speed conversion of event data to NumPy arrays for scientific analysis
Full support for FCS versions 2.0, 3.0, and 3.1 file parsing and creation
Integrated preprocessing for gain scaling and logarithmic transformations
Advanced metadata extraction from TEXT and ANALYSIS file segments
Robust support for multi-dataset FCS files and offset discrepancy correction
3,718 GitHub stars

Use Cases

Automating the preprocessing of large flow cytometry datasets for machine learning training
Converting proprietary instrument data into CSV or DataFrames for custom visualization
Validating and repairing corrupted FCS files with offset or header discrepancies