Can I use this to create new FCS files from scratch?

Yes, FlowIO provides the create_fcs function which allows you to generate new FCS 3.1 files from NumPy arrays, channel names, and custom metadata.

Does FlowIO apply data scaling automatically?

Yes, when preprocessing is enabled, FlowIO automatically applies gain scaling and logarithmic transformations based on the PnG and PnE keywords found in the file metadata.

How does FlowIO handle files with multiple datasets?

The skill includes specialized support for multi-dataset files via the read_multiple_data_sets function, allowing you to iterate through and process every dataset within a single file.

Can I read just the metadata without loading the entire event dataset?

Yes, you can initialize FlowData with the only_text=True parameter to perform a memory-efficient read of only the TEXT segment metadata.

Which FCS versions does FlowIO support?

FlowIO supports FCS versions 2.0, 3.0, and 3.1, covering the most common standards used in modern flow cytometry.

Flow Cytometry Data Handler (FlowIO)

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

byworkonthestreets

0•

Data Science & ML

Parses and manipulates Flow Cytometry Standard (FCS) files to extract event data and metadata for scientific data pipelines.

FlowIO is a specialized skill for handling Flow Cytometry Standard (FCS) files versions 2.0 through 3.1. It provides robust tools for extracting event data as NumPy arrays, accessing rich metadata from TEXT segments, and converting cytometry data into structured formats like pandas DataFrames or CSVs. Whether you're building backend services for clinical labs or pre-processing datasets for machine learning, this skill streamlines the interaction with complex binary biological data formats while handling common file errors, offset discrepancies, and multi-dataset structures.

Key Features

01Robust error handling for malformed files and offset discrepancies

020 GitHub stars

03Support for FCS versions 2.0, 3.0, and 3.1

04Read and write capabilities for creating new FCS files with custom metadata

05Automated preprocessing including gain scaling and log transformation

06Extraction of event data as NumPy arrays for scientific computing

Use Cases

01Automated extraction of instrument metadata and channel labels for laboratory reports

02Converting raw FCS files into CSV or DataFrame formats for downstream statistical analysis

03Preprocessing flow cytometry data for ingestion into machine learning models

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add workonthestreets/wolo flowio

For use in Claude.ai and ChatGPT