Xuran Wang

The paper uses STARMAPS to analyze single-cell RNA sequencing data and identifies spatial and temporal patterns of ASD-associated gene enrichment in specific brain regions.

This study identifies ADARB1 overexpression in T21 fetal brain due to trisomy 21, leading to increased A-to-I RNA editing and altered GRIA gene recoding.

This study develops a method to generate diverse GABAergic neurons from PSCs using TFs and signaling inhibition, showing distinct subtype compositions and disease relevance.

The paper optimizes Perturb-seq and uses structural topic modeling to identify shared impacts of ASD genes on neural development, revealing effects on cell fate and differentiation.

MuSiC2 improves cell type proportion estimation in bulk RNA-seq data from different clinical conditions using an iterative method, outperforming traditional approaches.

The paper introduces SCEPTRE, a method for analyzing single-cell CRISPR screens that improves calibration and sensitivity by addressing technical confounders through conditional resampling.

This paper introduces BSCET, a method that detects cell-type-specific allelic expression imbalance using bulk RNA-seq data integrated with scRNA-seq-derived cell-type information, identifying genes related to type 2 diabetes progression.

The paper develops a non-parametric method to estimate cell-specific gene co-expression networks and applies it to study gene network differences in fetal brain cells related to autism spectrum disorder.

MuSiC deconvolves bulk RNA-seq data using single-cell RNA-seq reference to estimate cell type composition, outperforming existing methods in tissues with closely related cell types.