How does it handle files with multiple datasets?

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

Can I convert FCS files to CSV using this skill?

Yes, FlowIO can extract event data as a NumPy array, which can then be easily converted to a pandas DataFrame and exported to CSV format.

What FCS versions does FlowIO support?

FlowIO supports the most common Flow Cytometry Standard versions, including v2.0, v3.0, and v3.1.

Does FlowIO apply data transformations automatically?

Yes, when preprocessing is enabled, it automatically applies gain scaling, logarithmic transformations (PnE), and time scaling based on the file's metadata.

Is FlowIO a replacement for FlowKit?

FlowIO is a lightweight handler for reading and writing files. For advanced analysis like gating or compensation, it is recommended to use FlowIO in conjunction with FlowKit.

FlowIO Flow Cytometry Handler

Name: FlowIO Flow Cytometry Handler
Author: lifangda

bylifangda

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Ciencia de Datos y ML

Parses and manipulates Flow Cytometry Standard (FCS) files to extract event data as NumPy arrays and manage metadata.

Acerca de

FlowIO is a specialized skill designed for bioinformaticians and data scientists working with flow cytometry data. It provides a lightweight, high-performance interface for reading and writing Flow Cytometry Standard (FCS) files across versions 2.0, 3.0, and 3.1. By enabling the seamless conversion of complex cytometry data into NumPy arrays and pandas DataFrames, this skill simplifies the transition from raw instrument output to advanced analytical pipelines. It features robust handling for problematic file offsets, multi-dataset support, and automatic preprocessing of gain and logarithmic scaling.

Características Principales

Full support for FCS versions 2.0, 3.0, and 3.1
Extract event data directly into NumPy arrays for scientific computing
Deep metadata parsing from TEXT and ANALYSIS file segments
Automated preprocessing for gain scaling and logarithmic transformations
16 GitHub stars
Functionality to create and export new FCS files with custom metadata

Casos de Uso

Preprocessing raw flow cytometry data for machine learning and statistical analysis
Batch converting large directories of FCS files into CSV or DataFrame formats
Extracting instrument-specific metadata and channel labels for data cataloging

Acerca de

Características Principales

Full support for FCS versions 2.0, 3.0, and 3.1
Extract event data directly into NumPy arrays for scientific computing
Deep metadata parsing from TEXT and ANALYSIS file segments
Automated preprocessing for gain scaling and logarithmic transformations
16 GitHub stars
Functionality to create and export new FCS files with custom metadata

Casos de Uso

Preprocessing raw flow cytometry data for machine learning and statistical analysis
Batch converting large directories of FCS files into CSV or DataFrame formats
Extracting instrument-specific metadata and channel labels for data cataloging