Parse FCS files to extract event data and metadata into NumPy arrays or DataFrames.
npx skills add https://github.com/mkurman/tamux --skill flowio-mkurman
Please help me install this Agent Skill. Skill: flowio Source: https://github.com/mkurman/tamux/tree/main/skills/scientific-skills/flowio Command: npx skills add https://github.com/mkurman/tamux --skill flowio-mkurman
Flow cytometry data often arrives in FCS files that mix metadata, raw events, and analysis results. This skill provides a robust, Python-based solution to read, parse, and export FCS data for analysis pipelines.
Install the FlowIO package and load a sample FCS file to inspect channels and events.
High-intent search queries and answers about installing and using this skill.
You can parse FCS flow cytometry files using a Python-based parser that reads TEXT and DATA segments, extracting event data into NumPy arrays or Pandas DataFrames. It supports FCS versions 2.0 through 3.1.
Yes, flow cytometry event data can be extracted into a Pandas DataFrame. The parser reads raw events from the DATA segment and converts them into NumPy arrays or DataFrames for downstream analytics.
Python 3.9 or higher is required to parse FCS files using this approach. The environment also needs the FlowIO, NumPy, and Pandas libraries installed to manage channel names and event data extraction.
Handling multi-dataset FCS files involves reading the file structure to access multiple embedded datasets. The parser provides error handling for common issues while extracting metadata and event data across datasets.
Yes, new FCS files can be created from NumPy arrays. This process allows you to preserve or customize TEXT metadata and channel names while generating valid Flow Cytometry Standard files for downstream analysis.
When parsing flow cytometry files, available metadata includes standard TEXT segment parameters like PnN, PnS, and PnR, defining channel names, stains, and ranges alongside the raw event data extracted from the file.
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