Xueyi Dong

FLAMESv2 is a flexible R/Bioconductor package for analyzing long-read single-cell RNA-seq data, demonstrating field-leading performance in isoform analysis and cell-type identification.

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

The paper introduces two methods to handle group heteroscedasticity in pseudo-bulk single-cell RNA-seq data, showing improved error control and power compared to existing methods.

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

The paper develops FLT-seq and FLAMES to characterize full-length isoforms in single cells of human and mouse, identifying thousands of unannotated isoforms and linking them to functional modules and drug resistance mutations.

This study benchmarks 9 scRNA-seq preprocessing workflows, finding that while they vary in gene detection and quantification, downstream analysis with effective normalization and clustering methods leads to consistent clustering results.

This study analyzes nanopore long-read RNA-seq data using short-read tools to assess differential gene and isoform expression in synthetic and mouse datasets.

The paper benchmarks scRNA-seq analysis methods using synthetic datasets generated from mixed cancer cell lines, identifying methods that perform well across tasks like normalization, clustering, and integration.

scPipe provides a flexible R/Bioconductor pipeline for preprocessing scRNA-seq data, handling multiple barcoding strategies and generating quality reports for downstream analysis.