TL;DR
Comprehensive evaluations on five single-cell perturbation datasets demonstrate that URFPert out-performs state-of-the-art methods in unseen perturbation regimes, providing a powerful tool for interpreting.
Comprehensive evaluations on five single-cell perturbation datasets demonstrate that URFPert outperforms state-of-the-art methods in unseen perturbation regimes, providing a powerful tool for interpreting regulatory mechanisms.
PerturbBridge is proposed, a conditional latent Schrödinger Bridge framework that reformulates stochastic population transport over high-dimensional, sparse geneexpression profiles as bridge learning in a compact cell latent space, and achieves state-of-the-art performance in differential-expression recovery on both be...
PerturbLDM, a latent-diffusion framework for conditional generation of single-cell transcriptional responses, is introduced, showing support for conditional response generation across data scales and biological settings.
The results show that graph-based signal propagation is a biologically grounded alternative to latent-shift perturbation modeling and can improve the recovery of sparse perturbation-induced transcriptional effects.
Disease reprograms cells through changes in gene regulation, yet identifying these changes remains a major challenge. We introduce NetDes-Duo, a computational method that jointly infers transcription factor regulatory network models for two related conditions using scRNA-seq data. The networks are optimized to have min...
Inferring gene regulatory networks (GRNs) from single-cell RNA sequencing (scRNA-seq) data offers insights into cellular behavior, but is complicated by the lack of temporal information and the prevalence of dropout noise. To address these challenges, we present FlowGRN, a method that integrates conditional flow matchi...
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