Laleh Haghverdi

Exovelo estimates cell state velocities without gene-wise parameter fitting, improving accuracy over pseudotime-based methods using single-cell mRNA data.

CCLONE identifies cancer cells in scRNA-seq data by jointly analyzing SNVs and cell populations, showing its effectiveness in leukemia and lung cancer datasets.

Compound-SNE aligns t-SNE embeddings across multiple single-cell omics datasets while preserving local structures.

The paper benchmarks five cell type annotation methods and shows how reference dataset design impacts label transfer performance.

The paper addresses issues in RNA velocity estimation from single-cell RNA-seq data and introduces two new methods for reliable velocity inference and visualization.

This study uses single-nuclei transcriptomics to analyze human kidney injury responses, identifying four injury-associated cell states and showing similar gene expression changes between AKI patients with and without COVID-19.

This paper introduces a batch correction method for scRNA-seq data that matches mutual nearest neighbours, showing improved performance over existing methods on simulated and real data.