flowio

FCS file parsing extracts event data as NumPy arrays and metadata from Flow Cytometry Standard files. Use FlowData to load an FCS file, then call as_array() to convert events into NumPy format for downstream analysis or CSV export.

Yes. FCS files can be parsed to extract events and metadata, then converted to CSV for use in spreadsheets or data analysis tools. This enables integration with preprocessing workflows and statistical platforms that require tabular input.

FCS versions 2.0 through 3.1 are supported, including single- and multi-dataset files. The parser handles metadata extraction, channel information, and event counts across standard and complex FCS structures used in modern cytometry pipelines.

The parser provides robust handling of problematic FCS files with memory-efficient metadata reading and null-channel exclusion. It enables reliable batch preprocessing without data loss, making it suitable for production backend data pipelines.

Yes. You can write new FCS files from NumPy arrays with customizable metadata and descriptive channel names, enabling data validation, standardization, and creation of annotated output files for downstream cytometry workflows.

FCS parsing can be done programmatically with lightweight tools that extract metadata and events without heavy dependencies. This approach suits backend pipelines and analysis notebooks where memory efficiency and automation are priorities.