Journal
GIGASCIENCE
Volume 10, Issue 9, Pages -Publisher
OXFORD UNIV PRESS
DOI: 10.1093/gigascience/giab062
Keywords
genetic demultiplexing; single-cell RNA sequencing; cancer; high-grade serous ovarian cancer; lung adenocarcinoma; tumor mutational burden; computational methods; simulations; benchmarking
Categories
Funding
- National Institutes of Health
- National Cancer Institute [P30 CA042014]
- Huntsman Cancer Foundation
Ask authors/readers for more resources
The study evaluated the feasibility of using genetic demultiplexing tools in cancer tissue with a pooled experimental design, showing significant cost savings and performance effectiveness. However, caution should be taken regarding the impact of ambient RNA from cell debris.
Background: 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. Results: Here, we performed in silico benchmark evaluations by combining raw sequencing reads from multiple single-cell samples in high-grade serous ovarian cancer, which has a high copy number burden, and lung adenocarcinoma, which has a high tumor mutational burden. Our results confirm that genetic demultiplexing tools can be effectively deployed on cancer tissue using a pooled experimental design, although high proportions of ambient RNA from cell debris reduce performance. Conclusions: This strategy provides significant cost savings through pooled library preparation. To facilitate similar analyses at the experimental design phase, we provide freely accessible code and a reproducible Snakemake workflow built around the best-performing tools found in our in silico benchmark evaluations, available at https://github.com/lmweber/snp-dmx-cancer.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available