Ni Zhao

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 SMRmix, a mixed effect model for integrating multiple microbiome datasets to identify consistent community-level associations with outcomes.

The paper introduces a non-parametric test for homogeneity of multivariate dispersions, showing it has higher power than existing methods in certain situations.

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.

The paper evaluates 47 highly variable gene selection methods for scRNA-seq across 19 datasets and finds hybrid methods outperform individual methods, proposing a new approach called mixHVG.

CAFT is a compositional log-linear model for microbiome data with zero cells that improves differential abundance analysis by handling zeros as censored observations and controlling FDR.

This study shows that the F. prausnitzii strain UT1 alters gut microbiome composition and carbohydrate metabolism in C57BL/6 mice.

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.

MIDASim simulates realistic microbiome data by capturing taxon correlations, sparsity, and compositionality, outperforming existing methods in accuracy and efficiency.

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.

This study identifies 32 new breast cancer susceptibility loci through GWAS, with associations varying by tumor subtype and genetic correlations among subtypes.

The paper introduces a mixed-model approach to test genetic associations with cancer risk, incorporating tumor characteristics, and shows it outperforms existing methods in identifying heterogeneous associations.