@Article{biocell.2021.017296,
AUTHOR = {JOE R. DELANEY},
TITLE = {Aneuploidy: An opportunity within single-cell RNA sequencing analysis},
JOURNAL = {BIOCELL},
VOLUME = {45},
YEAR = {2021},
NUMBER = {5},
PAGES = {1167--1170},
URL = {http://www.techscience.com/biocell/v45n5/43076},
ISSN = {1667-5746},
ABSTRACT = {Single-cell sequencing data has transformed the understanding of biological heterogeneity. While many flavors
of single-cell sequencing have been developed, single-cell RNA sequencing (scRNA-seq) is currently the most prolific
form in published literature. Bioinformatic analysis of differential biology within the population of cells studied relies
on inferences and grouping of cells due to the spotty nature of data within individual cell scRNA-seq gene counts.
One biologically relevant variable is readily inferred from scRNA-seq gene count tables regardless of individual gene
representation within single cells: aneuploidy. Since hundreds of genes are present on chromosome arms, high-quality
inferences of aneuploidy can be made from scRNA-seq datasets. This viewpoint summarizes how utilization of these
bioinformatic pipelines can benefit scRNA-seq studies, particularly in oncology wherein aneuploidy is both rampant
and a hallmark of the studied disease. Awareness and use of these analytical pipelines will improve each field’s ability
to understand the studied diseases. Authors are encouraged to attempt these aneuploid analyses when reporting
scRNA-seq data, much like copy-number variants are commonly reported in bulk genome sequencing data.},
DOI = {10.32604/biocell.2021.017296}
}