Does this skill handle gating and compensation?

FlowIO is primarily focused on reading and writing FCS files. For advanced analysis like gating and compensation, it is best used as a companion to the FlowKit library.

Can I convert FCS files to CSV or Excel format?

Yes, this skill provides workflows to extract FCS event data into NumPy arrays or Pandas DataFrames, which can then be exported to CSV or other common data formats.

Can I create new FCS files from my own data?

Yes, FlowIO can generate standards-compliant FCS 3.1 files using NumPy arrays and custom metadata keywords.

Which FCS versions are supported by this skill?

FlowIO supports reading and parsing of FCS versions 2.0, 3.0, and 3.1.

How does it handle files with multiple datasets?

The skill includes specialized functions to detect and read multi-dataset FCS files, allowing you to access each dataset independently via its offset.

Flow Cytometry Data Processor

Name: Flow Cytometry Data Processor
Author: K-Dense-AI

byK-Dense-AI

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2,188

Ciencia de Datos y ML

Parses and manipulates Flow Cytometry Standard (FCS) files, converting biological data into NumPy arrays and CSV formats for scientific analysis.

Acerca de

FlowIO is a specialized skill designed for handling Flow Cytometry Standard (FCS) files across versions 2.0 to 3.1. It enables researchers and developers to extract event data as NumPy arrays, access rich instrument metadata, and create new FCS files with minimal dependencies. This tool is essential for building scientific data pipelines, validating cytometry files, and performing pre-processing workflows before advanced analysis. By automating the extraction of scatter, fluorescence, and time-series data, it bridges the gap between raw biological instrument data and modern Python-based analysis libraries.

Características Principales

Accesses comprehensive TEXT and ANALYSIS metadata segments
2,188 GitHub stars
Handles multi-dataset FCS files and complex offset discrepancies
Full support for FCS versions 2.0, 3.0, and 3.1
Creates and exports standards-compliant FCS files from custom datasets
Extracts event data directly into NumPy arrays or Pandas DataFrames

Casos de Uso

Converting proprietary instrument data to open formats like CSV or Parquet
Preprocessing raw flow cytometry data for machine learning models
Automated metadata extraction and quality control for high-throughput biological experiments

Acerca de

Características Principales

Accesses comprehensive TEXT and ANALYSIS metadata segments
2,188 GitHub stars
Handles multi-dataset FCS files and complex offset discrepancies
Full support for FCS versions 2.0, 3.0, and 3.1
Creates and exports standards-compliant FCS files from custom datasets
Extracts event data directly into NumPy arrays or Pandas DataFrames

Casos de Uso

Converting proprietary instrument data to open formats like CSV or Parquet
Preprocessing raw flow cytometry data for machine learning models
Automated metadata extraction and quality control for high-throughput biological experiments