Lieke Michielsen

The paper performs a federated cis-eQTL meta-analysis across single-cell datasets to identify cell-type-specific genetic effects and link them to disease mechanisms.

The paper creates a multi-species DVC cell atlas using snRNAseq, identifying cell types and novel populations in mouse and rat.

The paper introduces a spatial long-read sequencing method to study developmental splicing and polyadenylation changes in cortical layers and cell types, finding layer-specific and age-related regulatory patterns.

The paper develops a high-resolution spatial long-read sequencing method and software to analyze isoform variability in brain cell types, revealing both region-specific and cell-type-specific patterns.

The paper predicts cell-type-specific exon inclusion in human brain cells using RBP binding sites and genomic sequence, revealing more complex splicing mechanisms in neurons than glia.

The paper introduces treeArches, a framework for building and extending cell-type hierarchies by mapping new datasets to reference atlases and harmonizing annotations across studies.

This paper introduces TACTiCS, a model that matches cell types across species using protein embeddings and transfer learning, outperforming existing methods.

This paper introduces a model that predicts cell population-specific gene expression from genomic sequence, outperforming tissue-specific models and enabling GWAS variant prioritization and motif learning.

This paper introduces scHPL, a hierarchical progressive learning method for single-cell data that continuously updates cell identity classifications across multiple datasets while preserving original annotations.

The paper benchmarks 20 automatic cell identification methods for single-cell RNA-sequencing data across eight datasets, finding that SVM performs best overall.