Pei Wang

This study performs a cell-type-aware transcriptome-wide association study (TWAS) of mammographic density phenotypes, identifying novel genes and prioritizing known GWAS loci through cell-type-specific gene expression analysis.

sCCIgen generates synthetic spatial transcriptomics data with known cell-cell interaction features to evaluate analytical tools.

The paper introduces OpenPedCan, a harmonized multi-omic dataset of pediatric cancer patients, providing integrated genomic, transcriptomic, and proteomic data for research and clinical applications.

QuadST identifies interaction-changed genes in spatially resolved transcriptomics by analyzing distance-expression associations in cell-type pairs.

DAGBagM learns DAGs with mixed continuous and binary variables to identify prognostic protein biomarkers in ovarian cancer.

MiXcan performs cell-type-specific transcriptome-wide association studies, identifying more breast cancer susceptibility genes than conventional methods by considering cell-type heterogeneity.

The paper analyzes CPTAC phosphoproteomics data to identify kinase activity differences between tumor and normal tissues across cancers and develops a web portal for querying kinase activity scores and associated data.

This paper introduces LRnetST, a statistical method for inferring ligand-receptor interaction networks from spatial transcriptomic data of ovarian tumors, revealing common and distinct interactions across samples.

BayesDeBulk estimates immune and stromal cell composition from bulk proteomic and transcriptomic data, outperforming existing tools in synthetic and real data examples.

iProMix decomposes bulk proteomic data to identify ACE2-protein associations in epithelial cells, revealing sex-specific interferon pathways linked to ACE2 abundance in lung adenocarcinoma tissues.

An ensemble of six imputation methods, built through the NCI-CPTAC Proteogenomics DREAM Challenge, fills missing values in labeled LC-MS/MS proteomics data, helping most for low-abundance proteins.

ProTrack provides an interactive web tool for exploring CPTAC proteogenomic data across multiple cancer types, enabling users to query and visualize multi-omics profiles without coding.

ProNetView-ccRCC provides an interactive web tool to explore phosphopeptide co-expression networks in clear cell renal cell carcinoma using CPTAC data.

The paper introduces iProFun, a multi-omics tool to identify DNA copy number and methylation effects on proteogenomic traits in ovarian cancer, finding 500 and 122 genes with cis-associations.

The paper identifies VGF as a key regulator of Alzheimer’s disease using multiscale causal network models and validates its protective role in mouse models.

spaceMap identifies regulatory relationships between DNA copy number alterations and protein levels in breast and ovarian tumors, revealing functionally enriched networks and key cancer genes.