Jay S. Stanley

LMD: Cluster-Independent Multiscale Marker Identification in Single-cell RNA-seq Data

LMD identifies localized genes in single-cell RNA-seq data to characterize cellular diversity without batch correction, outperforming existing methods.

Multi-omic Characterization of HIV Effects at Single Cell Level across Human Brain Regions

This study uses single-nucleus multiome sequencing to identify HIV-associated transcriptional and epigenetic changes in brain regions of individuals with HIV, revealing altered glial cell states and pro-inflammatory signaling.

Principled PCA separates signal from noise in omics count data

BiPCA improves PCA for omics count data by adaptively rescaling noise, enhancing biological interpretation and reducing batch effects across multiple modalities.

Gene Trajectory Inference for Single-cell Data by Optimal Transport Metrics

GeneTrajectory infers gene trajectories instead of cell trajectories to uncover progressive gene dynamics in biological processes.

Quantifying the effect of experimental perturbations at single-cell resolution

This paper introduces a method to quantify perturbation effects at single-cell resolution using manifold-based likelihood estimation and vertex frequency clustering, achieving higher accuracy than existing methods.