Weiliang Qiu

A Model-Based Clustering via Mixture of Bayesian Hierarchical Models with Covariate Adjustment for Detecting Differentially Expressed Genes from Paired Design

This paper introduces a Bayesian hierarchical mixture model with covariate adjustment to detect differentially expressed genes in paired design whole-genome data, showing improved performance over existing methods.

The Lupus Epigenome Relates to Genetics, Transcription and Serological Profiles with Dependency on Molecular Subtypes and Informs Drug Discovery

This study identifies epigenetic differences in SLE linked to genetics, transcription, and serology, revealing novel drug targets and pathways.

powerEQTL: An R package and shiny application for sample size and power calculation of bulk tissue and single-cell eQTL analysis

The paper introduces powerEQTL, an R package and web tool for calculating sample size and power for bulk and single-cell eQTL analysis.

Novel Data Transformations for RNA-seq Data Analysis

The paper proposes eight RNA-seq data transformations to improve analysis accuracy, showing that limma+rv and limma+l2 perform best in simulations and real data.

New Statistical Methods for Constructing Robust Differential Correlation Networks

This paper introduces six new statistical tests for differential correlation networks that are computationally efficient and outperform existing methods in simulations and real data analysis.

Introduce a New Approach to Detect Genes Associated to Oral Squamous Cell Carcinoma

This paper introduces a new method to detect genes associated with oral squamous cell carcinoma by analyzing both mean and variance differences between cases and controls, identifying 456 differentially variable and 2,375 differentially expressed gene probes.

The Association between Alcohol Consumption and Telomere Length: A Meta-Analysis Focusing on Observational Studies

This paper conducts a meta-analysis of observational studies to assess the association between alcohol consumption and telomere length, finding a significant link with variability across study types and populations.

Detecting Differential Variable microRNAs via Model-Based Clustering

The paper introduces a model-based clustering method to detect differentially variable microRNAs, outperforming the probe-wise F test and identifying 7 novel microRNAs in hepatocellular carcinoma.