TY - JOUR
T1 - Effective methods for bulk RNA-seq deconvolution using scnRNA-seq transcriptomes
AU - Cobos, Francisco Avila
AU - Panah, Mohammad Javad Najaf
AU - Epps, Jessica
AU - Long, Xiaochen
AU - Man, Tsz-Kwong
AU - Chiu, Hua-Sheng
AU - Chomsky, Elad
AU - Kiner, Evgeny
AU - Krueger, Michael J.
AU - di Bernardo, Diego
AU - Voloch, Luis
AU - Molenaar, Jan
AU - van Hooff, Sander R.
AU - Westermann, Frank
AU - Jansky, Selina
AU - Redell, Michele L.
AU - Mestdagh, Pieter
AU - Sumazin, Pavel
N1 - Funding Information: The results published here are in part based upon data generated by the Therapeutically Applicable Research to Generate Effective Treatments initiative. The work was supported by CPRIT awards RP180674 and RP230120, European Union’s Horizon 2020 research and innovation program under grant agreement 826121, NCI awards R21CA223140 and R21CA286257, and Special Research Fund postdoctoral scholarship from Ghent University (BOF21/PDO/007). Cell mixtures were profiled by the BCM Single Cell Genomics Core, which is supported by NIH shared instrument grants S10OD018033, S10OD023469, S10OD025240, and P30EY002520. Funding Information: We thank Elena Denisenko and Alistair Forrest (Harry Perkins Institute of Medical Research, Australia) for providing the necessary information to match bulk and scnRNA-seq samples from the GSE141115 dataset, Alex Swarbrick, Kate Harvey, Sunny Wu, and Dan Roden (Garvan Institute of Medical Research, Australia) for providing information regarding the state of tissue for scRNAseq captures and the matching tissue that was used for bulk RNAseq (“breast” dataset), and Andras Heczey for providing Jurkat (J32) cells. Anahita Bishop was the primary editor of this article and managed its editorial process and peer review in collaboration with the rest of the editorial team. The review history is available as Additional file 7. Publisher Copyright: © 2023, The Author(s).
PY - 2023/12/1
Y1 - 2023/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85166147805&partnerID=8YFLogxK
U2 - https://doi.org/10.1186/s13059-023-03016-6
DO - https://doi.org/10.1186/s13059-023-03016-6
M3 - Article
C2 - 37528411
SN - 1474-7596
VL - 24
JO - Genome Biology
JF - Genome Biology
IS - 1
M1 - 177
ER -