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Single-cell trajectory inference for detecting transient events in biological processes.

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Summary

scTransient identifies transient gene expression events in single-cell data. This method detects short-lived regulatory programs crucial for cell development and differentiation.

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Area of Science:

  • Single-cell omics
  • Computational biology
  • Systems biology

Background:

  • Transient gene or protein expression changes are critical regulatory checkpoints in cellular transitions.
  • These short-lived expression events are often missed in sparse and noisy single-cell omics data.

Purpose of the Study:

  • To introduce scTransient, a novel computational method for detecting transient molecular events in single-cell expression data.
  • To enhance the analysis of dynamic cellular processes by quantifying fleeting regulatory programs.

Main Methods:

  • scTransient transforms single-cell expression profiles into continuous pseudotime signals.
  • It employs wavelet-based signal processing to isolate short-lived gene activity bursts.
  • A continuous wavelet transform is applied to assign a transient-event score (TES) to each gene.

Main Results:

  • Synthetic benchmarks show TES robustly recovers transient events across various parameters.
  • scTransient successfully detected transient events in induced neuron development and single-cell cell cycle datasets.
  • The method identified key proteins, including stem cell regulators and S-phase DNA replication factors.

Conclusions:

  • scTransient effectively detects transient molecular events that are often missed in traditional analyses.
  • The method extends trajectory inference beyond descriptive ordering to quantitative detection of regulatory programs.
  • scTransient provides a practical approach to uncover transient events driving cell development, differentiation, and disease.