J. Javier Díaz-Mejía

This paper introduces a method to align single-cell RNA data across species and tissues using adversarial learning, revealing common cell types like macrophages and fibroblasts.

The paper introduces scMARK, a benchmark dataset and framework to evaluate scRNA integration tools, finding that scVI is the only tool tested capable of integrating scRNA studies at scale.

This study compares five data integration methods for single-cell RNA sequencing of cancer samples, finding STACAS and fastMNN most effective in correcting batch effects while preserving biological variability.

CReSCENT provides a web-based toolkit for analyzing scRNA-seq data in cancer research, offering preconfigured pipelines and user-editable workflows for non-experts.

This study benchmarks four methods for assigning cell type labels to cell clusters in scRNA-seq data, showing high performance but variability in precision-recall across methods and datasets.

The paper introduces BFG-GI to map DNA damage-dependent genetic interactions in yeast by combining party mating and barcode fusion genetics, revealing new condition-specific interactions.