Single-cell RNA sequencing reveals cell-type specific eQTLs in peripheral blood mononuclear cells

Click on a Dataset ID in the table below to learn more, and to find out who to contact about access to these data

Dataset ID	Description	Technology	Samples
EGAD00001003459	Single cell transcriptomics of PBMCs of 47 donors from the Lifelines Deep cohort (general population, Northern part of the Netherlands). Cells of five or six different donors were pooled together in one sample pool, resulting in eight different sample pools. In total, 28.855 cells were captured and their transcriptomes were sequenced to an average depth of 74k. Genotype data was available for each donor, which allowed us to use the Demuxlet method that uses variable SNPs between the pooled individuals to determine which cell belongs to which individual. Since genotype information is lacking of 2 individuals, the transcriptome of only 45 individuals could be retrieved.	Illumina HiSeq 4000	8
EGAD00010001315	Single cell transcriptomics of PBMCs of 47 donors from the Lifelines Deep cohort (general population, Northern part of the Netherlands). Cells of five or six different donors were pooled together in one sample pool, resulting in eight different sample pools. In total, 28.855 cells were captured and their transcriptomes were sequenced to an average depth of 74k. Genotype data was available for each donor, which allowed us to use the Demuxlet method that uses variable SNPs between the pooled individuals to determine which cell belongs to which individual. Since genotype information is lacking of 2 individuals, the transcriptome of only 45 individuals could be retrieved.	Illumina HiSeq4000	45

Publications	Citations
Single-cell RNA sequencing identifies celltype-specific cis-eQTLs and co-expression QTLs. van der Wijst MGP, Brugge H, de Vries DH, Deelen P, Swertz MA, LifeLines Cohort Study, BIOS Consortium, Franke L. Nat Genet 50: 2018 493-497	162
Reconstruction of Cell-type-Specific Interactomes at Single-Cell Resolution. Mohammadi S, Davila-Velderrain J, Kellis M. Cell Syst 9: 2019 559-568.e4	36
Alignment of single-cell RNA-seq samples without overcorrection using kernel density matching. Chen M, Zhan Q, Mu Z, Wang L, Zheng Z, Miao J, Zhu P, Li YI. Genome Res 31: 2021 698-712	3
Hierarchical progressive learning of cell identities in single-cell data. Michielsen L, Reinders MJT, Mahfouz A. Nat Commun 12: 2021 2799	16
Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data. Li S, Schmid KT, de Vries DH, Korshevniuk M, Losert C, Oelen R, van Blokland IV, BIOS Consortium, sc-eQTLgen Consortium, Groot HE, Swertz MA, van der Harst P, Westra HJ, van der Wijst MGP, Heinig M, Franke L. Genome Biol 24: 2023 80	5