1 citations · 1 across the 7 of their papers we have counts for
8 papers
Data-Adaptive Grassmann Manifold Representations for Spatial Transcriptomics Alignment
Xiang Xiang Wang, Sean Cottrell, Guo-Wei Wei
Spatial transcriptomics measures gene expression together with spatial coordinates, but many existing analysis methods represent each spot primarily by a single feature vector. We…
Subspace Tensor Orthogonal Rotation Model (STORM) for Batch Alignment, Cell Type Deconvolution, and Gene Imputation in Spatial Transcriptomic Data
Sean Cottrell, Guo-Wei Wei, Longxiu Huang
Spatial transcriptomics data analysis integrates cellular transcriptional activity with spatial coordinates to identify spatial domains, infer cell-type dynamics, and characterize…
Multiscale Grassmann Manifolds for Single-Cell Data Analysis
Xiang Xiang Wang, Sean Cottrell, Guo-Wei Wei
Single-cell data analysis seeks to characterize cellular heterogeneity based on high-dimensional gene expression profiles. Conventional approaches represent each cell as a vector i…
Computational Drug Repurposing for Alzheimer's Disease via Sheaf Theoretic Population-Scale Analysis of snRNA-seq Data
Sean Cottrell, Seungmin Yoon, Xiaoqi Wei +2
Single-cell and single-nucleus RNA sequencing (scRNA-seq /snRNA-seq) are widely used to reveal heterogeneity in cells, showing a growing potential for precision and personalized me…
Meta-analysis and Topological Perturbation in Interactomic Network for Anti-opioid Addiction Drug Repurposing
Chunhuan Zhang, Sean Cottrell, Benjamin Jones +5
The ongoing opioid crisis highlights the urgent need for novel therapeutic strategies that can be rapidly deployed. This study presents a novel approach to identify potential repur…
Multiscale differential geometry learning of networks with applications to single-cell RNA sequencing data
Hongsong Feng, Sean Cottrell, Yuta Hozumi +1
Single-cell RNA sequencing (scRNA-seq) has emerged as a transformative technology, offering unparalleled insights into the intricate landscape of cellular diversity and gene expres…
K-Nearest-Neighbors Induced Topological PCA for scRNA Sequence Data Analysis
Sean Cottrell, Yuta Hozumi, Guo-Wei Wei
Single-cell RNA sequencing (scRNA-seq) is widely used to reveal heterogeneity in cells, which has given us insights into cell-cell communication, cell differentiation, and differen…
PLPCA: Persistent Laplacian Enhanced-PCA for Microarray Data Analysis
Sean Cottrell, Rui Wang, Guowei Wei
Over the years, Principal Component Analysis (PCA) has served as the baseline approach for dimensionality reduction in gene expression data analysis. It primary objective is to ide…