Freecyto

Freecyto provides scalable analysis and visualization of flow cytometry (FCM) datasets using weighted k-means quantization and SQL-based sub-population gating to enable segmentation and exploration of cellular events.

Key Features:

• Interactive data analysis: Supports interactive exploration and manipulation of FCM data for rapid analytical iteration.
• Data quantization (weighted k-means): Applies a weighted k-means clustering algorithm to quantize FCM data into representative subsets for efficient processing and visualization.
• Comprehensive visualization tools: Produces scatterplots (dotplots), histograms, heatmaps, and boxplots for fluorescence emission and light scatter signals (events).
• SQL-based sub-population gating: Implements SQL-based gating for detailed segmentation and querying of cell sub-populations.
• Preservation of data accuracy: Maintains high data accuracy relative to conventional FCM software such as FlowJo.
• Scalable handling of large datasets: Enables analysis of large-scale FCM datasets by deriving and using representative subsets from quantized data.

Scientific Applications:

• Population identification and gating: Segmentation and identification of cell sub-populations within FCM datasets using SQL-based gating.
• Exploratory visualization of events: Visualization of fluorescence emission and light scattering measurements (events) via scatterplots, histograms, heatmaps, and boxplots.
• Scalable analysis of large FCM datasets: Reduction of data complexity through weighted k-means quantization to enable analysis of high-volume cytometry experiments.
• Comparative accuracy assessment: Benchmarking and comparison of analysis results against conventional FCM software such as FlowJo.

Methodology:

Data quantization is performed using a weighted k-means clustering algorithm to produce representative subsets of events, and sub-population segmentation is performed using SQL-based gating.