Rachel Thijssen

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 study compares single-nucleus and single-cell RNA sequencing in human bone marrow mononuclear cells, identifying method-specific biases and trade-offs.

This study shows that CLL cells rapidly adapt to venetoclax by upregulating pro-survival proteins and cytokines, suggesting combination therapies targeting these pathways could improve treatment outcomes.

GLMsim simulates single cell RNA-seq data incorporating library size, biology, and batch effects, enabling benchmarking of integration methods.

The paper introduces scRaCH-seq, a method for single-cell long-read sequencing that improves detection of isoform usage and mutations in targeted genes, demonstrating its application in CLL samples.

RUV-III-NB normalizes scRNA-seq data by removing unwanted variation from both cell embeddings and gene-level counts, improving differential expression and clustering results across multiple datasets.

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.