Shiquan Sun

Powerful and accurate detection of temporal gene expression patterns from multi-sample multi-stage single cell transcriptomics data with TDEseq

TDEseq detects temporal gene expression patterns in multi-sample, multi-stage single-cell transcriptomics data with improved power and calibration.

Testing and controlling for horizontal pleiotropy with the probabilistic Mendelian randomization in transcriptome-wide association studies

This paper introduces PMR-Egger, a probabilistic Mendelian randomization method to test and control horizontal pleiotropy in transcriptome-wide association studies, showing improved type I error control and power.

Statistical Analysis of Spatial Expression Pattern for Spatially Resolved Transcriptomic Studies

The paper introduces SPARK, a statistical method for detecting spatially expressed genes in spatial transcriptomic data, showing it is up to ten times more powerful than existing approaches.

Accuracy, Robustness and Scalability of Dimensionality Reduction Methods for Single Cell RNAseq Analysis

This study evaluates 18 dimensionality reduction methods on 30 scRNA-seq datasets to assess their accuracy, robustness, and scalability for cell clustering and lineage reconstruction.

IMAGE:High-powered detection of genetic effects on DNA methylation using integrated methylation QTL mapping and allele-specific analysis

The paper introduces IMAGE, a statistical method for mQTL mapping that improves detection of genetic effects on DNA methylation by accounting for bisulfite sequencing data and allele-specific patterns.

Heritability Estimation and Differential Analysis with Generalized Linear Mixed Models in Genomic Sequencing Studies

This paper introduces PQLseq, a method for heritability estimation and differential analysis in genomic sequencing studies, showing it provides unbiased heritability estimates and better power for differential analysis.

Differential Expression Analysis for RNAseq using Poisson Mixed Models

This paper introduces a Poisson mixed model for differential expression analysis in RNAseq, accounting for over-dispersion and sample non-independence, showing improved power in real data.