Does FlowIO handle files with multiple datasets?

Yes, the skill includes dedicated functionality to detect and iterate through multiple datasets within a single FCS file using next-data offsets.

What versions of FCS does FlowIO support?

FlowIO supports Flow Cytometry Standard versions 2.0, 3.0, and 3.1, covering the vast majority of modern instrument outputs.

Does it support data preprocessing like logarithmic scaling?

By default, FlowIO applies standard FCS preprocessing including gain scaling, time scaling, and PnE logarithmic transformations to ensure data accuracy.

Can I extract metadata without loading all event data?

Yes, you can use the 'only_text=True' parameter when initializing FlowData to perform memory-efficient metadata extraction from the TEXT segment without loading large arrays.

FlowIO Flow Cytometry Handler

Name: FlowIO Flow Cytometry Handler
Author: Microck

byMicrock

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데이터 과학 및 ML

Parses and writes Flow Cytometry Standard (FCS) files, enabling seamless conversion of biological data into NumPy arrays and dataframes for scientific analysis.

소개

FlowIO is a specialized Python skill designed for the robust handling of Flow Cytometry Standard (FCS) files versions 2.0 through 3.1. It provides researchers and developers with a lightweight yet powerful interface to extract event data directly into NumPy arrays, parse rich TEXT segment metadata, and generate compliant FCS files from scratch. This skill is essential for building automated bioinformatics pipelines, validating cytometry data integrity, and preprocessing raw biological measurements for downstream machine learning and statistical analysis.

주요 기능

Deep metadata parsing including instrument keywords and channel labels
Comprehensive support for FCS versions 2.0, 3.0, and 3.1
Direct extraction of event data into optimized NumPy arrays
81 GitHub stars
Built-in preprocessing for gain scaling and logarithmic transformations
Memory-efficient reading modes for metadata-only inspection

사용 사례

Generating synthetic FCS files for testing instrument software and algorithms
Building backend data pipelines for high-throughput cytometry file processing
Automating the conversion of FCS files to CSV or Pandas DataFrames for analysis

소개

주요 기능

Deep metadata parsing including instrument keywords and channel labels
Comprehensive support for FCS versions 2.0, 3.0, and 3.1
Direct extraction of event data into optimized NumPy arrays
81 GitHub stars
Built-in preprocessing for gain scaling and logarithmic transformations
Memory-efficient reading modes for metadata-only inspection

사용 사례

Generating synthetic FCS files for testing instrument software and algorithms
Building backend data pipelines for high-throughput cytometry file processing
Automating the conversion of FCS files to CSV or Pandas DataFrames for analysis