Xinlei Wang

BOSE estimates sample mean and standard deviation from median, range, and interquartile range using Bayesian order statistics, showing robustness and improved performance in small samples and non-normal distributions.

G-SPRI predicts cancer driver events from missense mutations by integrating structural and mutation-specific features using a graph model.

BiGER is a Bayesian rank aggregation method for meta-analysis of gene lists, handling mixed rankings and missing data with high accuracy.

The paper develops eTRex, a variational Bayesian model, to analyze 4,819 ATAC-seq datasets and create a pan-cancer atlas of transcriptional regulator profiles, revealing common and context-specific regulators.

MisTIC corrects transcript misassignment in spatial transcriptomics data, improving cell type identification, differential expression analysis, and RNA localization.

CytoOne is a probabilistic framework for CyTOF data that integrates batch correction, differential analysis, and visualization, effectively modeling sparse and noisy data.

BIT identifies transcriptional regulators using a Bayesian hierarchical model, improving accuracy and interpretability by integrating epigenomic data with TR ChIP-seq libraries.

The paper introduces a Bayesian framework to map cell-cell interactions from spatial transcriptomics data, revealing new biological insights in cancer and immune responses.

This paper reviews and benchmarks NGS-based computational methods for predicting transcriptional regulators using query gene sets, finding that BART, ChIP-Atlas, and Lisa perform best.

MetaNorm improves normalization of NanoString nCounter data by incorporating meta-analytic priors, enhancing stability and computational efficiency.

Cytomulate simulates CyTOF data accurately and efficiently, outperforming existing methods in capturing data characteristics and learning distributions.

This paper benchmarks 21 dimension reduction methods on CyTOF data, finding SAUCIE, SQuaD-MDS, and scvis as top performers with different strengths.

This paper develops SClineger, a Bayesian model to correct genetic drop-outs in scRNA-Seq data for accurate lineage tracing across tissues and time scales.