Genetic demultiplexing of pooled single-cell RNA-sequencing samples in cancer facilitates effective experimental design

摘要: Abstract Pooling cells from multiple biological samples prior to library preparation within the same single-cell RNA sequencing experiment provides several advantages, including lower costs and reduced unwanted technological variation, such as batch effects. Computational demultiplexing tools based on natural genetic variation between individuals provide a simple approach demultiplex samples, which does not require complex additional experimental procedures. However, these have been evaluated in cancer, where somatic variants, could differ sample, may obscure variation. Here, we performed silico benchmark evaluations by combining reads high-grade serous ovarian has high copy number burden, lung adenocarcinoma, tumor mutational confirming that can be effectively deployed cancer tissue using pooled design. We demonstrate this strategy significant cost savings through preparation. To facilitate similar analyses at design phase, freely accessible code reproducible Snakemake workflow built around best-performing found our evaluations, available https://github.com/lmweber/snp-dmx-cancer.