DEP-Track: Single-Cell Dielectrophoresis Trajectories and Crossover Frequencies

A computational framework for automated cell tracking and crossover frequency estimation from dielectrophoresis microscopy data. The dataset includes results from long-term time-lapse imaging experiments involving 13,200 frames and hundreds of tracked cells. It was authored by Sena Lee and last updated on 2026-04-23.

Use Cases

• Training machine learning models for automated cell trajectory association based on motion-aware tracking.
• Estimating single-cell dielectric properties based on extracted crossover frequency data.
• Benchmarking new cell tracking algorithms against the framework's anchor-free detection and trajectory preservation.
• Conducting population-scale statistical analysis of cell behavior under frequency-modulated dielectrophoresis.

Strengths

• Includes data from a long-term experiment with 13,200 frames, enabling analysis of motion transitions.
• Framework validated against conventional analysis workflows, suggesting analytical accuracy.
• Enables extraction of crossover frequency estimates from repeated events within a single experiment, reducing the need for repeated runs.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The primary data file is a DOCX document (3.7 MB), which may require parsing to extract structured data.

Provenance

Source

figshare

Collection Method

Generated from frequency-modulated dielectrophoresis microscopy experiments and processed by the DEP-Track computational framework.

Freshness

Last updated 2026-04-23 05:35:14; freshness should be verified.