Michael C. Wu

Constructing microbiome co-occurrence networks with confidence: A conditional, nonparametric, inference-based approach

The paper introduces a nonparametric method to construct microbiome co-occurrence networks with confidence, improving network recovery by accounting for conditional associations and providing statistical inference.

Topological Data Analysis of Spatial Protein Expression in Multiplexed Spatial Proteomics Studies

The paper introduces TOASTER, a method that uses topological data analysis to associate spatial protein expression with patient outcomes without relying on cell segmentation or phenotyping.

Detecting Clinically Relevant Topological Structures in Multiplexed Spatial Proteomics Imaging Using TopKAT

This paper introduces TopKAT, a topology-based method to detect associations between spatial cell structures and clinical outcomes in spatial proteomics data.

A Spatial Omnibus Test (SPOT) for Spatial Proteomic Data

The paper introduces SPOT, a method to assess spatial clustering in proteomic data across multiple radii, and demonstrates its application in ovarian cancer.

Weighted variance component test for the integrative multi-omics analysis of microbiome data

The paper introduces a weighted variance components framework to integrate metagenomic and metabolomic data for pathway analysis, showing improved power while controlling type I error.

Kernel-based genetic association analysis for microbiome phenotypes identifies host genetic drivers of beta-diversity

The paper introduces a kernel-based method to identify host genetic variants associated with microbiome beta-diversity, finding immunity-related genes influence gut microbiota composition.

Batch effects removal for microbiome data via conditional quantile regression (ConQuR)

This paper introduces ConQuR, a method to remove batch effects in microbiome data by using conditional quantile regression, demonstrating its effectiveness in preserving signals and improving analysis.

Random-effect based test for multinomial logistic regression: choice of the reference level and its impact on the testing

The paper investigates the impact of reference level choice on multinomial logistic regression random-effect score tests and proposes invariant procedures.

Identifying individual risk rare variants using protein structure-guided local tests (POINT)

The paper introduces POINT, a method that uses protein structure to identify individual rare variants associated with complex diseases by performing localized association tests.