Which FCS versions does FlowIO support?

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

Can I convert FCS data to a pandas DataFrame?

Yes, you can extract event data as a NumPy array using the as_array() method and then load it into a pandas DataFrame using the provided channel labels.

Can I use FlowIO for high-throughput backend pipelines?

Yes, FlowIO is a lightweight library with minimal dependencies, making it highly suitable for backend services, automated data pipelines, and cloud-based preprocessing.

Does this skill handle files with multiple datasets?

Yes, the skill includes specialized workflows to detect and read multi-dataset FCS files using the read_multiple_data_sets function.

Is data preprocessing like log scaling supported?

Absolutely. FlowIO can automatically apply gain scaling and logarithmic transformations (PnE) based on the file metadata during the data extraction process.

FlowIO Cytometry Handler

Name: FlowIO Cytometry Handler
Author: x-cmd

byx-cmd

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数据科学与机器学习

Parses and creates Flow Cytometry Standard (FCS) files for seamless integration with Python data science workflows.

关于

FlowIO is a lightweight, specialized tool designed to bridge the gap between flow cytometry hardware outputs and modern data science environments. It enables researchers and developers to parse FCS metadata, extract event data as NumPy arrays, and generate new FCS files across versions 2.0 to 3.1. By offering built-in features for gain scaling, logarithmic transformation, and multi-dataset support, FlowIO simplifies the preprocessing of complex biological data, making it ready for analysis in pandas, NumPy, or machine learning pipelines without the overhead of heavy GUI-based software.

主要功能

Parses comprehensive TEXT and ANALYSIS metadata segments
8 GitHub stars
Extracts event data directly into high-performance NumPy arrays
Automates data preprocessing including gain and log scaling
Supports FCS versions 2.0, 3.0, and 3.1
Handles multi-dataset FCS files and complex offset discrepancies

使用场景

Converting proprietary FCS files to CSV or pandas DataFrames for analysis
Preprocessing raw cytometry data for machine learning models
Generating synthetic or filtered FCS files for experimental validation

关于

主要功能

Parses comprehensive TEXT and ANALYSIS metadata segments
8 GitHub stars
Extracts event data directly into high-performance NumPy arrays
Automates data preprocessing including gain and log scaling
Supports FCS versions 2.0, 3.0, and 3.1
Handles multi-dataset FCS files and complex offset discrepancies

使用场景

Converting proprietary FCS files to CSV or pandas DataFrames for analysis
Preprocessing raw cytometry data for machine learning models
Generating synthetic or filtered FCS files for experimental validation