flowCut

flowCut identifies and removes technical artifacts from flow cytometry event data to improve data quality for downstream analyses such as gating.

Key Features:

• Implementation: Implemented as an R package for programmatic preprocessing of flow cytometry data.
• Automated anomaly detection: Automatically detects anomalous events arising from technical issues such as clogging or fluorescence intensity shifts.
• Outlier event removal: Precisely removes detected outlier events to reduce bias in downstream gating analyses.
• Time-versus-fluorescence file flagging: Employs time versus fluorescence analysis to flag files that contain problematic data segments.
• Performance evaluation: The original evaluation reported that flowCut outperforms existing automated approaches for detecting technical artifacts in flow cytometry data.

Scientific Applications:

• Gating accuracy: Improves the reliability of manual and automated gating by removing acquisition-related artifacts.
• Immunology and cell biology studies: Provides higher-quality event data for experiments relying on flow cytometry measurements in immunology and cell biology.
• Preprocessing for downstream analysis: Serves as a preprocessing step to reduce technical noise before further quantitative analyses of flow cytometry datasets.

Methodology:

Performs time-versus-fluorescence analysis to detect anomalous events and automatically removes flagged outlier events from flow cytometry datasets.