Wodan Ling

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.

This paper introduces QuanT, a non-parametric method to identify unmeasured heterogeneity in microbiome data, improving downstream analysis accuracy.

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

MOSAIC is a pipeline that compares microbiome integration strategies, finding that ComBat and ConQuR families perform best overall in balancing artifact removal and signal preservation.

SurvBal identifies microbiome taxon balances associated with survival outcomes using survival models and step-wise selection.

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.

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.

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.