Wenyi Wang

The paper introduces the ATLAS penalty for spatially smooth deconvolution in spatial transcriptomics, showing improved spatial domain detection using auxiliary covariates.

TmSNet predicts tumor cell-specific total mRNA expression from multi-omic data, achieving high accuracy across cancer types and external cohorts.

The paper develops DeMixNB, a deconvolution model for sparse-count RNA sequencing data, showing its utility in analyzing tumor cell plasticity in breast and lung cancer.

The paper develops TLSCP, a transfer learning method for survival-based clustering of predictors, applied to TP53 mutation annotation, showing improved clustering and clinical interpretability.

The paper introduces SARP models to cluster spatially resolved transcriptomics data and identifies co-localized immune and stromal cell subtypes in colorectal cancer.

The paper develops dependent mixture models to compare immune cell subtypes between early-onset and late-onset colorectal cancer, identifying distinct and shared T cell profiles.

The paper develops CliPP to estimate subclonal mutation load (sML) and shows it predicts survival and immunotherapy response in cancers with low to moderate TMB.

The paper introduces MuSE2.0, a tool that accelerates somatic mutation calling for whole-genome and whole-exome sequencing data from heterogeneous tumor samples, achieving significant speed improvements over existing methods.

DeMixSC improves deconvolution accuracy by using single-cell RNA-seq and a benchmark dataset to account for technological discrepancies in complex tissue samples.

The paper develops a Bayesian semi-parametric model to predict cancer recurrence risk in survivors, showing good predictive performance for second primary cancers.

This study identifies molecular changes in bladder mucosa that initiate cancer, revealing inflammation's role and mutation patterns linked to early carcinogenesis.

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.

CliPP reconstructs subclonal architecture in cancer DNA sequencing data efficiently and accurately, outperforming existing methods in speed while maintaining high concordance with consensus calls.

The paper quantifies tumor-specific total mRNA expression from bulk data and shows its association with clinical outcomes across 15 cancer types.

The paper introduces Famdenovo, a model to predict de novo mutations in families with Li-Fraumeni syndrome, identifying 40 new carriers and showing high prediction accuracy.

The paper estimates penetrance for first and second primary cancer diagnoses in Li-Fraumeni syndrome families and validates a risk prediction model with an AUC of 0.77.

The paper estimates cancer-specific penetrance in Li-Fraumeni syndrome using a statistical model and validates it across multiple cohorts, showing age-dependent risk patterns for different cancer types.

The paper presents an R pipeline using DeMixT for tumor-stroma-immune transcriptome deconvolution, showing immune proportion variations in COAD and PRAD datasets.

Cardelino integrates whole exomes and single-cell transcriptomes to identify clone-of-origin and reveal phenotypic impacts of somatic variants in human fibroblasts.

DeMixT improves transcriptomic deconvolution by estimating component proportions and expression profiles in heterogeneous samples, showing high accuracy in validation and TCGA data.

This paper introduces an RNA-seq mixture experiment using two cancer cell lines to benchmark library preparation kits and analysis methods, finding that total RNA kits and voom with quality weights perform best.