Wenbo Guo

SpatialZoomer: multi-scale feature analysis of spatial transcriptomics

SpatialZoomer identifies disease progression signals and spatially heterogeneous niches in spatial transcriptomics data by analyzing features at multiple scales.

scTrace+: enhance the cell fate inference by integrating the lineage-tracing and multi-faceted transcriptomic similarity information

scTrace+ improves cell fate inference by integrating lineage-tracing data with transcriptomic similarities using a Kernelized Probabilistic Matrix Factorization model, accurately predicting cell fates and identifying driver genes.

cytoGPNet: Enhancing Clinical Outcome Prediction Accuracy Using Longitudinal Cytometry Data in Small Cohort Studies

cytoGPNet improves clinical outcome prediction using longitudinal cytometry data in small cohorts by addressing challenges in sample variability, temporal analysis, robustness, and interpretability.

uniLIVER: a Human Liver Cell Atlas for Data-Driven Cellular State Mapping

uniLIVER creates a human liver cell atlas using sc/snRNA-seq data and develops LiverCT to map abnormal datasets to the reference atlas, revealing changes in cell type composition and states in liver cancer.

scCancer2: data-driven in-depth annotations of the tumor microenvironment at single-level resolution

scCancer2 provides a supervised framework for annotating tumor microenvironment cells across datasets and evaluates subtype relationships using scRNA-seq and bulk data.

Decoding functional cell-cell communication events by multi-view graph learning on spatial transcriptomics

This paper introduces HoloNet, a method to decode functional cell-cell communication events from spatial transcriptomic data by integrating ligand-receptor pairs, cell-type spatial distribution, and downstream gene expression.

TIST: Transcriptome and Histopathological Image Integrative Analysis for Spatial Transcriptomics

TIST integrates spatial transcriptomics data with histopathological images to improve spatial cluster identification and gene expression analysis.

New gene association measures by joint network embedding of multiple gene expression datasets

The paper introduces JEBIN, a method to integrate multiple gene expression datasets into a low-dimensional gene representation, showing improved performance in co-expression network analysis and differential co-expression studies.

scCancer: a package for automated processing of single cell RNA-seq data in cancer

scCancer processes and analyzes scRNA-seq data for cancer research, identifying cell populations, estimating malignancy scores, and analyzing intra-tumor heterogeneity.