Parse FCS 2.0-3.1 cytometry files into event arrays and metadata.
npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill flowio-jasrajtulsi
Please help me install this Agent Skill. Skill: flowio Source: https://github.com/jasrajtulsi/GRAD-SCOPE/tree/main/.claude/skills/flowio Command: npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill flowio-jasrajtulsi
FlowIO removes the friction of working with Flow Cytometry Standard files by turning opaque FCS data into readable metadata, channel labels, and event arrays that can be used immediately in downstream analysis.
It reads FCS 2.0, 3.0, and 3.1 files, extracts TEXT and ANALYSIS metadata, classifies scatter, fluorescence, and time channels, and converts events into NumPy arrays or pandas DataFrames. It also supports multi-dataset files, offset discrepancy handling, null channel filtering, and exporting modified data back to FCS or CSV workflows. A practical use case is reviewing a batch of cytometry acquisitions, checking channel structure and acquisition settings, and saving cleaned event data for preprocessing before compensation or gating.
Use the flowio skill to inspect an FCS file, summarize its metadata and channels, and convert the event data into an analysis-ready array.
High-intent search queries and answers about installing and using this skill.
You can parse FCS cytometry files into analysis-ready pandas DataFrames or NumPy arrays by extracting structured event arrays and metadata, handling offset discrepancies and null channel filtering during conversion.
Yes, batch preprocessing workflows support extracting TEXT and ANALYSIS metadata while classifying scatter, fluorescence, and time channels across multiple FCS datasets for efficient cytometry acquisition review.
Yes, this FCS parser handles offset discrepancies, supports multi-dataset files, and performs null channel exclusion during preprocessing to ensure accurate event array extraction and metadata conversion.
The best way to convert FCS event data to CSV is to parse cytometry files into structured NumPy arrays or pandas DataFrames and export the modified event data directly to CSV workflows.
Yes, you can export modified event arrays back to new FCS files. This supports file validation, conversion, and batch preprocessing workflows after inspecting and altering the original cytometry data.
Excluding null channels during flow cytometry preprocessing ensures empty or unused parameters do not skew event arrays, keeping the extracted NumPy arrays and DataFrames clean for downstream compensation or gating analysis.
Estrella-231Parse FCS 2.0–3.1 files to extract event data and metadata.
yf8578Parse and create FCS files, converting event data to NumPy arrays or Pandas DataFrames.
mkurmanParse FCS files to extract event data and metadata into NumPy arrays or DataFrames.
fuzzy-dynamicsParse and write FCS files into NumPy arrays or CSV.
