Which FCS versions are supported by this skill?

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

Can I convert FCS files to CSV using FlowIO?

Yes, FlowIO can extract event data as NumPy arrays, which can then be easily converted to a Pandas DataFrame and exported as a CSV file.

Does FlowIO apply data transformations like log scaling?

Yes, when preprocessing is enabled, FlowIO automatically applies gain scaling and logarithmic transformations based on the PnE and PnG keywords in the file.

How does this skill handle files with multiple datasets?

The skill includes specialized functions like read_multiple_data_sets to detect and process FCS files containing more than one data segment.

Is this skill suitable for advanced gating analysis?

FlowIO is primarily for file reading and writing. For advanced analysis like gating or compensation, it is best used as a data provider for libraries like FlowKit.

FlowIO FCS Handler

Name: FlowIO FCS Handler
Author: BbgnsurfTech

byBbgnsurfTech

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Data Science & ML

Parses and manages Flow Cytometry Standard (FCS) files to extract event data, metadata, and convert datasets for scientific analysis.

About

FlowIO is a specialized Python utility designed for seamless interaction with Flow Cytometry Standard (FCS) files across versions 2.0 through 3.1. This skill enables researchers and developers to extract event data directly into NumPy arrays, parse complex TEXT segment metadata, and generate new FCS files from scratch. It is particularly useful for bioinformatics pipelines, allowing for the conversion of raw cytometry data into analysis-ready formats like Pandas DataFrames while handling specialized tasks such as gain scaling, logarithmic transformations, and multi-dataset file management.

Key Features

Comprehensive support for FCS versions 2.0, 3.0, and 3.1
3 GitHub stars
Full parsing of metadata keywords and channel information
Robust error handling for offset discrepancies and multi-dataset files
Direct extraction of event data into NumPy arrays for data science
Creation of new FCS files from synthetic or modified data

Use Cases

Converting raw flow cytometry data to CSV or Pandas for machine learning models
Preprocessing and filtering event data before advanced gating analysis
Automating batch metadata extraction and file validation in scientific pipelines

About

Key Features

Comprehensive support for FCS versions 2.0, 3.0, and 3.1
3 GitHub stars
Full parsing of metadata keywords and channel information
Robust error handling for offset discrepancies and multi-dataset files
Direct extraction of event data into NumPy arrays for data science
Creation of new FCS files from synthetic or modified data

Use Cases

Converting raw flow cytometry data to CSV or Pandas for machine learning models
Preprocessing and filtering event data before advanced gating analysis
Automating batch metadata extraction and file validation in scientific pipelines