flowio-flow-cytometry

You can parse FCS files by extracting event data and channel metadata directly into NumPy arrays. This process supports Flow Cytometry Standard versions 2.0 through 3.1, enabling programmatic access for bioinformatics preprocessing and analysis pipelines.

The best way to generate new FCS files is by creating them from existing NumPy arrays and specified channel information. This allows you to output valid Flow Cytometry Standard files for downstream experiments or data sharing.

Yes, you can use NumPy alongside pandas to extract channel metadata and event data from flow cytometry files. The process parses FCS file metadata, allowing you to load cytometry event data into arrays for further analysis.

Yes, this FCS parsing approach supports older flow cytometry file versions ranging from 2.0 to 3.1. It reads event data and channel metadata across these versions to ensure compatibility with legacy cytometry datasets.

To batch process a directory of .fcs files for data extraction, iterate through the files to parse event data into NumPy arrays. This extracts channel metadata and event data from each file for further analysis and visualization.