Genetic demultiplexing of pooled single-cell RNA-sequencing samples in cancer facilitates effective experimental design
Author(s) -
Lukas M. Weber,
Ariel A. Hippen,
Peter F. Hickey,
Kristofer C. Berrett,
Jason Gertz,
Jennifer A. Doherty,
Casey S. Greene,
Stephanie C. Hicks
Publication year - 2021
Publication title -
gigascience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.947
H-Index - 54
ISSN - 2047-217X
DOI - 10.1093/gigascience/giab062
Subject(s) - in silico , computer science , pooling , computational biology , benchmark (surveying) , biology , genetics , gene , artificial intelligence , geodesy , geography
Pooling cells from multiple biological samples prior to library preparation within the same single-cell RNA sequencing experiment provides several advantages, including lower library preparation costs and reduced unwanted technological variation, such as batch effects. Computational demultiplexing tools based on natural genetic variation between individuals provide a simple approach to demultiplex samples, which does not require complex additional experimental procedures. However, to our knowledge these tools have not been evaluated in cancer, where somatic variants, which could differ between cells from the same sample, may obscure the signal in natural genetic variation.
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