Jingshu Wang

Transcriptional perturbation effects remain structured beyond the leading global trend

The paper shows that transcriptional perturbations have structured effects beyond a global trend, with specific patterns transferable across datasets.

Uncovering Pseudotime-Varying Genetic Causal Effects Along Single-Cell Trajectories for Pulmonary Disease Trait

This study identifies pseudotime-dependent causal effects of genes on pulmonary fibrosis using scRNA-seq and GWAS data.

Causal mediation analysis for time-varying heritable risk factors with Mendelian Randomization

FLOW-MR estimates causal effects of time-varying risk factors using GWAS summary statistics, revealing BMI's childhood protective effect on breast cancer and evolving impacts on stroke risk.

Joint Trajectory Inference for Single-cell Genomics Using Deep Learning with a Mixture Prior

This paper introduces VITAE, a deep learning method for trajectory inference in single-cell genomics that improves accuracy and uncertainty quantification across datasets.

Validity and Interpretation of Two-Sample Mendelian Randomization with Binary Traits

This paper provides a formal statistical justification for using two-sample Mendelian randomization with binary traits by showing that standard methods estimate scaled causal effects on an underlying liability scale.

A multiplex platform to identify mechanisms and modulators of proteotoxicity in neurodegeneration

The paper develops a multiplex reverse genetics platform to identify genetic modifiers of protein misfolding in neurodegenerative diseases and identifies DNAJB6 as a modifier of toxicity and solubility of ALS/FTD-linked RNA-binding proteins.

Causal Inference for Heritable Phenotypic Risk Factors Using Heterogeneous Genetic Instruments

This paper introduces GRAPPLE, a framework for causal inference using heterogeneous genetic instruments, and applies it to study the causal effects of blood lipids, BMI, and systolic blood pressure on 25 diseases.

A Mendelian randomization study of the role of lipoprotein subfractions in coronary artery disease

This study uses Mendelian randomization to assess the causal effects of lipoprotein subfractions on coronary artery disease risk, finding protective effects for certain HDL particle traits.

Surface protein imputation from single cell transcriptomes by deep neural networks

The paper introduces a deep learning framework to impute surface protein abundances from scRNA-seq data using transfer learning.

Data Denoising with transfer learning in single-cell transcriptomics

This paper improves scRNA-seq data quality using transfer learning by combining a deep autoencoder with a Bayesian model to extract gene-gene relationships across diverse datasets.

Gene Expression Distribution Deconvolution in Single Cell RNA Sequencing

The paper introduces DESCEND, a method to deconvolve true gene expression distributions from noisy scRNA-seq data, improving estimates of dispersion and burstiness while adjusting for covariates.

SAVER: Gene expression recovery for UMI-based single cell RNA sequencing

SAVER improves gene expression estimates in scRNA-seq by imputing zeros and enhancing low/moderate expression genes, improving downstream analysis accuracy.