Does this skill handle files with multiple datasets?

Yes, it includes specific functionality to detect, list, and read multiple datasets contained within a single FCS file.

Does it automatically handle log scaling and gain?

Yes, FlowIO applies standard FCS preprocessing transformations, including gain scaling and logarithmic transformations, when reading data unless explicitly disabled.

What FCS versions are supported by this skill?

This skill supports Flow Cytometry Standard (FCS) versions 2.0, 3.0, and 3.1.

Is it possible to create new FCS files from NumPy data?

Absolutely. You can use the create_fcs function to generate valid FCS files from NumPy arrays while specifying custom channel names and metadata.

Can I convert FCS files to CSV format?

Yes, FlowIO allows you to extract event data as NumPy arrays, which can then be easily exported to CSV or converted into pandas DataFrames.

FlowIO Flow Cytometry Handler

Name: FlowIO Flow Cytometry Handler
Author: Zehong-Wang

byZehong-Wang

0•

数据科学与机器学习

Parses, processes, and generates Flow Cytometry Standard (FCS) files for biological data analysis and bioinformatics pipelines.

FlowIO is a lightweight Python utility designed to handle Flow Cytometry Standard (FCS) files across versions 2.0 to 3.1. It enables researchers and developers to extract event data into NumPy arrays, manage complex metadata segments, and create new FCS files from scratch. This skill is essential for building bioinformatics workflows, performing data preprocessing for machine learning, or converting specialized cytometry data into interoperable formats like CSV and pandas DataFrames, providing a robust bridge between raw instrument output and modern data science stacks.

主要功能

010 GitHub stars

02Full support for FCS versions 2.0, 3.0, and 3.1

03Deep metadata parsing from TEXT and ANALYSIS segments

04Advanced handling for multi-dataset files and offset discrepancies

05Ability to create and export custom FCS files with specific channel labels

06Extraction of event data into high-performance NumPy arrays

使用场景

01Converting raw flow cytometry data into pandas DataFrames for statistical analysis

02Preprocessing high-dimensional cytometry datasets for machine learning model training

03Building automated quality control pipelines for biological laboratory instruments

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add zehong-wang/kosmos flowio

For use in Claude.ai and ChatGPT

Download Skill