Xinzhou Ge

SnakeAltPromoter provides a reproducible framework for differential alternative promoter analysis using RNA-seq, benchmarked against CAGE data and scalable for large datasets.

The paper introduces BatchQC, an R package for assessing and correcting batch effects in high-throughput data with reproducible tools and workflows.

This study benchmarks five cell-type-specific spatially variable gene detection methods using a simulation framework, revealing performance issues in realistic settings and providing a user guide for method selection.

ClipperQTL identifies eGenes efficiently by reducing permutations needed for large datasets while maintaining performance comparable to FastQTL.

The paper identifies extensive isoform diversity in the developing human neocortex and links it to neuropsychiatric disorder risk mechanisms.

APIR aggregates database search algorithms for proteomics to increase peptide identification while controlling FDR, outperforming individual methods on standard and real data.

This study benchmarks five methylation-based methods for cell type deconvolution of plasma cell-free DNA using deep whole-genome bisulfite sequencing data from 35 human cell types.

ClusterDE identifies reliable markers and validates clusters in omics data by controlling false discovery rates through synthetic control, improving accuracy in single-cell, spatial, and bulk transcriptomics.

The paper re-evaluates differential expression methods on semi-synthetic RNA-seq data without normalization, finding the Wilcoxon rank-sum test still outperforms dearseq in FDR control and power.

The paper shows that popular differential expression methods like DESeq2 and edgeR have high false discovery rates in population-level RNA-seq data, recommending the Wilcoxon rank-sum test instead.

Clipper provides a p-value-free FDR control method for high-throughput data from two conditions, outperforming existing approaches without relying on p-values or distributional assumptions.

DORGE identifies cancer driver genes by integrating genetic and epigenetic data, finding histone modifications predict TSGs and missense mutations predict OGs.

An ensemble of six imputation methods, built through the NCI-CPTAC Proteogenomics DREAM Challenge, fills missing values in labeled LC-MS/MS proteomics data, helping most for low-abundance proteins.

EpiAlign is a new alignment tool for comparing chromatin state sequences across epigenomic data, identifying common patterns and cell-type-specific features.