flowio

Flow cytometry FCS files can be parsed to extract event data and metadata by reading supported FCS versions 2.0 through 3.1. This process retrieves detailed channel information and experimental parameters, outputting event data as NumPy arrays for analysis.

You can generate new FCS files directly from NumPy arrays to create standardized datasets for bioinformatics pipelines. This allows you to prepare custom flow cytometry data with specific metadata for seamless integration and data sharing setups.

This FCS file handler supports multi-dataset files and various FCS versions ranging from 2.0 to 3.1. It implements robust error handling to ensure accurate processing of complex flow cytometry datasets across different format specifications.

The best way to convert raw flow cytometry data into analyzable formats like CSV is by parsing the FCS file to access event data as a NumPy array. Once extracted, this structured data can be easily exported for downstream analysis.

Metadata extraction works by reading the FCS file headers to retrieve detailed information about channels and experimental parameters. This allows you to access critical flow cytometry metadata needed for accurate data interpretation and pipeline integration.

There are no significant limitations for multi-dataset files, as the handler implements robust error handling specifically designed for complex flow cytometry datasets. It accurately processes multiple datasets within a single FCS file to support research workflows.