Lin Hou

MOD-scTWAS: Leveraging gene co-expression for single-cell transcriptome-wide association studies

MOD-scTWAS improves single-cell TWAS by jointly modeling gene co-expression modules, enhancing GReX prediction accuracy and identifying more significant cell-type-gene-trait associations.

SpatialZoomer: multi-scale feature analysis of spatial transcriptomics

SpatialZoomer identifies disease progression signals and spatially heterogeneous niches in spatial transcriptomics data by analyzing features at multiple scales.

Dissecting Tissue Architecture and Function through Mapping of Cellular Communication Networks

The paper introduces a method to infer cell-cell communication networks from spatial transcriptome data and demonstrates its effectiveness in identifying communication hotspots and tissue architecture in a mouse brain dataset.

Inference of heterogeneous effects in single-cell genetic perturbation screens

This paper introduces scCAPE, a tool for analyzing heterogeneous genetic perturbation effects in single-cell data, and identifies novel interactions in erythroid differentiation.

Cofea: correlation-based feature selection for single-cell chromatin accessibility data

Cofea selects biologically relevant features from single-cell chromatin accessibility data by focusing on correlations between accessible regions, improving downstream analysis.

Quantifying portable genetic effects and improving cross-ancestry genetic prediction with GWAS summary statistics

This paper introduces X-Wing, a framework that improves genetic prediction across populations using GWAS summary statistics by identifying portable genetic effects.

Differential Analysis of RNA Structure Probing Experiments at Nucleotide Resolution: Uncovering Regulatory Functions of RNA Structure

DiffScan identifies structurally variable regions in RNA structure probing data with improved accuracy across multiple platforms.

Quantifying concordant genetic effects of de novo mutations on multiple disorders

This paper introduces EncoreDNM, a statistical framework to quantify shared genetic effects of de novo mutations between disorders using exome-wide summary data, revealing abundant pairwise enrichment correlations.

Detecting Local Genetic Correlations with Scan Statistics

This paper introduces LOGODetect, a statistical method to identify local genetic correlation signals across multiple phenotypes using GWAS summary statistics, finding 49 non-overlapping genome regions associated with psychiatric disorders.