Yunshun Chen

This study identifies dysregulated epithelial cell death signaling in inflammatory bowel disease, showing it persists during remission despite therapy.

hoodscanR identifies and analyzes cell neighborhoods in spatial transcriptomics data, revealing transcriptional differences in tumor cells across neighborhoods.

SpatialBench evaluates spatial transcriptomics platforms using a mouse spleen model, finding consistent biological signals but varying resolution across technologies.

The paper provides an open-source online book for analyzing spatial transcriptomics data using Bioconductor in R, with reproducible code and datasets.

The paper introduces limma and edgeR pipelines for differential transcript usage analysis that account for read-to-transcript ambiguity, improving power and FDR control in small and large datasets.

The paper introduces scider, a spatial analysis framework that preserves tissue structure by identifying regions of interest using kernel density estimation, enabling multi-sample analysis and revealing spatial patterns.

The paper introduces a new method for differential splicing detection that incorporates exon-junction reads, improving statistical power while controlling false discovery rates.

stPipe provides a flexible R/Bioconductor pipeline for preprocessing spatial transcriptomics data from multiple platforms, enabling standardized analysis and benchmarking.

edgeR v4 adds a continuous negative binomial for fractional counts, bias-corrected quasi-likelihood for small counts, C-based fitting, and new transcript, exon, methylation and fold-change-threshold analyses.

The paper benchmarks spatial transcriptomics technologies using a multi-sample dataset from mouse spleen tissue, finding improved data quality with probe-based capture methods and CytAssist.

The paper examines spatial transcriptomic and proteomic profiles of Hofbauer cells in placentas with and without past Plasmodium falciparum infection, finding no significant differences in HBC features but reduced fetal vessel development in infected placentas.

This paper shows that dividing out quantification uncertainty from transcript counts improves differential expression analysis using edgeR.

The paper demonstrates a workflow for trajectory inference and time course analysis of scRNA-seq data from mouse mammary gland, identifying genes associated with pseudotime.

This study develops a synthetic de-methylating module to reactivate stably silenced genes by combining TET1's demethylating activity with transcriptional activators.

The paper benchmarks long-read RNA-sequencing tools using synthetic data to evaluate isoform detection and differential expression analysis performance.

The paper provides R code and processed data objects to reproduce analyses of a scRNA-seq atlas of human breast tissue across normal, preneoplastic, and tumorigenic states.