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

Transient changes in gene or protein expression often mark the key regulatory checkpoints that propel cells from one functional state to the next, yet they are easy to miss in sparse, noisy single-cell omics data. We introduce scTransient, which transforms single-cell expression profiles into continuous pseudotime signals and uses wavelet-based signal processing to isolate short-lived but biologically meaningful bursts of gene activity. After ordering cells with supervised pseudotime, scTransient windows expression values with supervised pseudotime, applies a continuous wavelet transform, and assigns every gene a transient-event score (TES) that rewards sharp, isolated coefficients while penalizing background fluctuations. Synthetic benchmarks demonstrate that TES robustly recovers transient events (TE) across a wide range of parameters, including cell numbers, signal-to-noise ratios, and event widths. Applying scTransient to two real datasets-induced neuron development and single-cell cell cycle-demonstrates scTransient's ability to detect TEs along pseudotime and identify proteins known to be related to the biological process under study. These include stem cell regulators in induced neuronal development and S-phase DNA replication factors in A549 cells. By extending trajectory inference from descriptive ordering to quantitative detection of fleeting regulatory programs, scTransient offers a practical route to uncover transient molecular events that drive development, differentiation, and disease.