Qian Li

BiNVaST improves cell typing across spatial transcriptomic platforms by generating synthetic data that preserves niche boundaries and integrates molecular and spatial information.

iModMix identifies novel multi-omics associations by constructing data-driven modules without relying on biological annotations.

MODE improves deconvolution by integrating multi-omic data through a multimodal autoencoder, outperforming existing methods in simulation and real data.

imply improves cell-type deconvolution by using personalized reference panels, showing reduced bias and better alignment with biological facts in real data.

The paper introduces a method for deconvolving bulk proteomes using transcriptome-proteome integration and applies it to identify cell-type-specific protein quantitative trait loci.

The paper introduces a joint model with matching and regularization to detect OTU-specific compositional trajectories predictive of disease onset in metagenomic data.

This study investigates the role of short-chain fatty acids (SCFAs) in schizophrenia, showing their association with immune activation and contribution to pathophysiology in both humans and mice.

lncDIFF detects differentially expressed long non-coding RNAs with higher power and lower false discovery rate compared to existing methods.