Heaton H., Talman Arthur, Knights A., Imaz M., Gaffney D. J., Durbin R., Hemberg M., Lawniczak M. K. N. (2020). Souporcell : robust clustering of single-cell RNA-seq data by genotype without reference genotypes. Nature Methods, 17 (6), p. 615-620. ISSN 1548-7091.
Titre du document
Souporcell : robust clustering of single-cell RNA-seq data by genotype without reference genotypes
Heaton H., Talman Arthur, Knights A., Imaz M., Gaffney D. J., Durbin R., Hemberg M., Lawniczak M. K. N.
Source
Nature Methods, 2020,
17 (6), p. 615-620 ISSN 1548-7091
Souporcell clusters single-cell RNA-seq data using genotype information without the use of a genotype reference. Methods to deconvolve single-cell RNA-sequencing (scRNA-seq) data are necessary for samples containing a mixture of genotypes, whether they are natural or experimentally combined. Multiplexing across donors is a popular experimental design that can avoid batch effects, reduce costs and improve doublet detection. By using variants detected in scRNA-seq reads, it is possible to assign cells to their donor of origin and identify cross-genotype doublets that may have highly similar transcriptional profiles, precluding detection by transcriptional profile. More subtle cross-genotype variant contamination can be used to estimate the amount of ambient RNA. Ambient RNA is caused by cell lysis before droplet partitioning and is an important confounder of scRNA-seq analysis. Here we develop souporcell, a method to cluster cells using the genetic variants detected within the scRNA-seq reads. We show that it achieves high accuracy on genotype clustering, doublet detection and ambient RNA estimation, as demonstrated across a range of challenging scenarios.
Plan de classement
Sciences fondamentales / Techniques d'analyse et de recherche [020]
