Qingnan Liang

This study compares imaging-based spatial transcriptomics platforms using FFPE tumor samples, finding differences in performance and highlighting factors affecting data quality.

The paper constructs immune-specific gene sets using non-negative matrix factorization on bulk RNA-seq data to improve immune subtype analysis and ICB response prediction.

This paper introduces LSGI, a framework to identify interpretable spatial gradients in spatial transcriptomics data, revealing pathway gradients in glioblastoma and across 87 tumor datasets.

GSDensity provides pathway-centric analysis of single-cell and spatial transcriptomics data without clustering, revealing novel cell-pathway associations and spatially relevant pathways in cancer and brain development.

DeMixSC improves deconvolution accuracy by using single-cell RNA-seq and a benchmark dataset to account for technological discrepancies in complex tissue samples.

This paper presents a comprehensive single-cell multi-omics atlas of the human retina, integrating RNA-seq and ATAC-seq data to identify cell-type-specific cis-element-gene relationships and transcription factor regulons.

The paper performs spatial transcriptomic profiling of the mouse retina to uncover its laminar organization and generates a comprehensive spatial single cell reference atlas.

The paper identifies cell type-specific genetic effects on gene regulation in human retina using single-cell multiomics data and finds that trans-factors, not cis-elements, drive these effects.

The paper models scRNA-seq data from developmental processes using SDEs to identify transition cells that explain heterogeneity and predict cell fate in early tumorigenesis.

The paper introduces the Van Elteren test to improve identification of differentially expressed genes in single-cell data by addressing batch effects, showing higher sensitivity and specificity than existing methods.

This study uses single-nuclei RNA-seq to profile human retinal tissue, identifying cell-type-specific gene expression and improving disease gene prioritization compared to bulk RNA-seq and mouse models.