Publications

An overview of the sc-eQTLGen Consortium papers.

• Li, s., et al. (2023). Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data - Genome Biology https://doi.org/10.1186/s13059-023-02897-x
• Oelen, R., et al. (2022). Single-cell RNA-sequencing reveals widespread personalized, context-specific gene expression regulation in immune cells - Nature Communications https://doi.org/10.1038/s41467-022-30893-5
• Neavin, D. (2022). Demuxafy: Improvement in droplet assignment by integrating multiple single5 cell demultiplexing and doublet detection methods - bioRxiv https://doi.org/10.1101/2022.03.07.483367
• Michielsen, L. (2021). Hierarchical progressive learning of cell identities in single-cell data - Nature Communications
  https://doi.org/10.1038/s41467-021-23196-8
• Cuomo, A.S.E. et al. (2021). Optimizing expression quantitative trait locus mapping workflows for single-cell studies - Genome Biology https://doi.org/10.1186/s13059-021-02407-x
• de Vries, DH., et al. (2020). Integrating GWAS with bulk and single-cell RNA-sequencing reveals a role for LY86 in the anti-Candida host response - PLoS pathogens https://doi.org/10.1371/journal.ppat.1008408
• van der Wijst, M., et al. (2019). Single-cell eQTLGen Consortium: a personalized understanding of disease - eLife https://doi.org/10.7554/eLife.52155
• van der Wijst, M., et al. (2018). Single-cell RNA sequencing identifies celltype-specific cis-eQTLs and co-expression QTLs - Nature Genetics https://doi.org/10.1038/s41588-018-0089-9