Gagnon, J.; Pi, L.; Ryals, M.; Wan, Q.; Hu, W.; Ouyang, Z.; Zhang, B.; Li, K. Recommendations of scRNA-seq Differential Gene Expression Analysis Based on Comprehensive Benchmarking. Life2022, 12, 850.
Gagnon, J.; Pi, L.; Ryals, M.; Wan, Q.; Hu, W.; Ouyang, Z.; Zhang, B.; Li, K. Recommendations of scRNA-seq Differential Gene Expression Analysis Based on Comprehensive Benchmarking. Life 2022, 12, 850.
Gagnon, J.; Pi, L.; Ryals, M.; Wan, Q.; Hu, W.; Ouyang, Z.; Zhang, B.; Li, K. Recommendations of scRNA-seq Differential Gene Expression Analysis Based on Comprehensive Benchmarking. Life2022, 12, 850.
Gagnon, J.; Pi, L.; Ryals, M.; Wan, Q.; Hu, W.; Ouyang, Z.; Zhang, B.; Li, K. Recommendations of scRNA-seq Differential Gene Expression Analysis Based on Comprehensive Benchmarking. Life 2022, 12, 850.
Abstract
To guide analysts to select the right tool and parameters in differential gene expression analysis of single-cell RNA sequencing (scRNA-seq) data, we developed a novel simulator that recapitulates the data characteristics of real scRNA-seq datasets while accounting for all the relevant sources of variation in a multi-subject, multi-condition scRNA-seq experiment: the cell-to-cell variation within a subject, the variation across subjects, the variability across cell types, the mean/variance relationship of gene expression across genes, library size effects, group effects, and covariate effects. By applying it to benchmark 12 differential gene expression analysis methods (including cell-level and pseudo-bulk methods) on simulated multi-condition, multi-subject data of the 10x Genomics platform, we demonstrated that methods originating from the negative binomial mixed model such as glmmTMB and NEBULA-HL outperformed other methods. Utilizing NEBULA-HL in a statistical analysis pipeline (https://github.com/interactivereport/scRNAseq_DE) for single cell analysis will enable scientists to better understand cell-type specific transcriptomic response to disease or treatment effects and to discover new drug targets. Further, application to two real datasets showed the outperformance of our differential expression (DE) pipeline, with unified findings of differentially expressed genes (DEG) and a pseudo-time trajectory transcriptomic result. In the end, we made recommendations of filtering strategies of cells and genes based on simulation results to achieve optimal experimental goals.
Computer Science and Mathematics, Computational Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
