Generative Modeling of Single Cell Gene Expression for Dose-Dependent Chemical Perturbations

Analysis of Cell–Cell Communication by Single-Nuclei RNA Sequencing Identifies AHR-Mediated Induction of NRG-ERBB Signaling

“…AHR activation promotes the accumulation of hepatic lipids (steatosis), which can progress to steatohepatitis with fibrosis in a dose-and time-dependent manner [3][4][5][6]. A previous snRNAseq study identified and characterized dose-dependent and cell-specific responses in the AHRmediated progression of steatosis to steatohepatitis with fibrosis following treatment with TCDD [3][4][5][6]19,35]. To further investigate disrupted cell signaling in AHR-mediated NAFLD development, hepatic-snRNAseq and spatial-transcriptomic data were examined to infer ligand-receptor interactions using CellChat [17].…”

A mini-review on perturbation modelling across single-cell omic modalities

“…scVIDR [57] applies linear and log-linear interpolation on vector arithmetics to calculate differentially expressed genes in a cell-type-specific fashion ( contrary to scGEN, which calculates vector arithmetics in the latent space in a non-cell-type specific manner ) and extrapolate perturbation effects to unseen cell types and drug dosages, respectively. To approximate the function of the decoder, scVIDR uses ridge regression, which provides an explainability “flavour” to the latent space of the VAE architecture by generating “gene scores”.…”

“…Regarding the complex generative models, the standout computational tool is scVIDR, which not only exceeded the performance of the traditional scGEN but also outperformed CellOT and scPreGan based on R2 metrics [57] . Meanwhile, OntoVAE shows comparable performance to VEGA and expiMap in terms of ARI metric [65] , and sVAE+ surpassed both β-VAE and the standard VAE when comparing Pearson MCC scores [72] .…”