Can I convert FCS files to CSV using this skill?

Yes, the skill provides clear workflows to extract event data as NumPy arrays, which can then be easily converted to Pandas DataFrames and exported to CSV format.

Can I create new FCS files from scratch?

Absolutely. You can use the create_fcs function to generate valid FCS 3.1 files from NumPy arrays and define your own custom metadata and channel labels.

Which FCS versions does FlowIO support?

FlowIO supports FCS versions 2.0, 3.0, and 3.1, making it compatible with most modern and legacy cytometry instruments.

Does it handle files with multiple datasets?

Yes, FlowIO includes specific functionality to detect and read multi-dataset FCS files using the read_multiple_data_sets() utility.

Does FlowIO apply data preprocessing automatically?

By default, FlowIO applies standard gain scaling and logarithmic transformations (PnE) to the data, though users can access raw data by setting preprocess=False.

FlowIO (Flow Cytometry Data Handler)

Name: FlowIO (Flow Cytometry Data Handler)
Author: Sologa

bySologa

Ciencia de Datos y ML

Parses and generates Flow Cytometry Standard (FCS) files to facilitate cytometry data preprocessing and scientific analysis.

Acerca de

FlowIO is a specialized skill designed for handling Flow Cytometry Standard (FCS) files across versions 2.0, 3.0, and 3.1. It enables developers and researchers to seamlessly extract metadata, convert event data into NumPy arrays or DataFrames, and generate new FCS files from scratch. This skill is particularly valuable for bioinformatics pipelines, laboratory data processing, and preparing cytometry datasets for advanced machine learning or statistical analysis without the overhead of heavy dependencies.

Características Principales

Comprehensive metadata and channel information parsing
Ability to create and export new FCS files with custom metadata
Extraction of event data as NumPy arrays for scientific computing
Support for FCS versions 2.0, 3.0, and 3.1
0 GitHub stars
Advanced handling of multi-dataset files and offset discrepancies

Casos de Uso

Automated validation and metadata inspection of large laboratory datasets
Converting FCS files to CSV or Pandas DataFrames for batch analysis
Preprocessing raw cytometry data for machine learning models

Acerca de

Características Principales

Comprehensive metadata and channel information parsing
Ability to create and export new FCS files with custom metadata
Extraction of event data as NumPy arrays for scientific computing
Support for FCS versions 2.0, 3.0, and 3.1
0 GitHub stars
Advanced handling of multi-dataset files and offset discrepancies

Casos de Uso

Automated validation and metadata inspection of large laboratory datasets
Converting FCS files to CSV or Pandas DataFrames for batch analysis
Preprocessing raw cytometry data for machine learning models