Language
English
Publication Date
8-1-2023
Journal
Genome Biology
DOI
10.1186/s13059-023-03016-6
PMID
37528411
PMCID
PMC10394903
PubMedCentral® Posted Date
8-1-2023
PubMedCentral® Full Text Version
Post-print
Abstract
Background: RNA profiling technologies at single-cell resolutions, including single-cell and single-nuclei RNA sequencing (scRNA-seq and snRNA-seq, scnRNA-seq for short), can help characterize the composition of tissues and reveal cells that influence key functions in both healthy and disease tissues. However, the use of these technologies is operationally challenging because of high costs and stringent sample-collection requirements. Computational deconvolution methods that infer the composition of bulk-profiled samples using scnRNA-seq-characterized cell types can broaden scnRNA-seq applications, but their effectiveness remains controversial.
Results: We produced the first systematic evaluation of deconvolution methods on datasets with either known or scnRNA-seq-estimated compositions. Our analyses revealed biases that are common to scnRNA-seq 10X Genomics assays and illustrated the importance of accurate and properly controlled data preprocessing and method selection and optimization. Moreover, our results suggested that concurrent RNA-seq and scnRNA-seq profiles can help improve the accuracy of both scnRNA-seq preprocessing and the deconvolution methods that employ them. Indeed, our proposed method, Single-cell RNA Quantity Informed Deconvolution (SQUID), which combines RNA-seq transformation and dampened weighted least-squares deconvolution approaches, consistently outperformed other methods in predicting the composition of cell mixtures and tissue samples.
Conclusions: We showed that analysis of concurrent RNA-seq and scnRNA-seq profiles with SQUID can produce accurate cell-type abundance estimates and that this accuracy improvement was necessary for identifying outcomes-predictive cancer cell subclones in pediatric acute myeloid leukemia and neuroblastoma datasets. These results suggest that deconvolution accuracy improvements are vital to enabling its applications in the life sciences.
Keywords
Child, Humans, Transcriptome, RNA-Seq, Gene Expression Profiling, RNA, Small Interfering, Sequence Analysis, RNA, Single-Cell Analysis
Published Open-Access
yes
Recommended Citation
Cobos, Francisco Avila; Panah, Mohammad Javad Najaf; Epps, Jessica; et al., "Effective Methods for Bulk RNA-Seq Deconvolution Using scnRNA-Seq Transcriptomes" (2023). Faculty, Staff and Students Publications. 4485.
https://digitalcommons.library.tmc.edu/baylor_docs/4485
Graphical Abstract
Included in
Biomedical Informatics Commons, Genetic Phenomena Commons, Genetic Processes Commons, Genetic Structures Commons, Internal Medicine Commons, Medical Genetics Commons