Shaolong Cao

The paper uses spatial transcriptomics to analyze cell-cell interactions in a cuprizone model, finding region-specific changes and partial translatability to multiple sclerosis lesions.

SpaceSequest provides a unified pipeline for analyzing spatial transcriptomics data from multiple platforms, enabling standardized processing, analysis, and visualization.

This study compares snRNAseq data from C9orf72, SOD1, and sporadic ALS spinal cords, identifying shared and distinct molecular features across cell types.

ScaleSC provides a GPU-accelerated single-cell RNA-seq analysis pipeline with 20x speedup and scalability for large datasets.

The paper develops CliPP to estimate subclonal mutation load (sML) and shows it predicts survival and immunotherapy response in cancers with low to moderate TMB.

This study identifies molecular changes in bladder mucosa that initiate cancer, revealing inflammation's role and mutation patterns linked to early carcinogenesis.

CliPP reconstructs subclonal architecture in cancer DNA sequencing data efficiently and accurately, outperforming existing methods in speed while maintaining high concordance with consensus calls.

The paper quantifies tumor-specific total mRNA expression from bulk data and shows its association with clinical outcomes across 15 cancer types.

The paper presents an R pipeline using DeMixT for tumor-stroma-immune transcriptome deconvolution, showing immune proportion variations in COAD and PRAD datasets.

DeMixT improves transcriptomic deconvolution by estimating component proportions and expression profiles in heterogeneous samples, showing high accuracy in validation and TCGA data.