Name
Abstract | ||
|---|---|---|
Motivation: Recent advancements in single-cell RNA sequencing (scRNA-seq) have enabled time-efficient transcriptome profiling in individual cells. To optimize sequencing protocols and develop reliable analysis methods for various application scenarios, solid simulation methods for scRNA-seq data are required. However, due to the noisy nature of scRNA-seq data, currently available simulation methods cannot sufficiently capture and simulate important properties of real data, especially the biological variation. In this study, we developed scRNA-seq information producer (SCRIP), a novel simulator for scRNA-seq that is accurate and enables simulation of bursting kinetics. Results: Compared to existing simulators, SCRIP showed a significantly higher accuracy of stimulating key data features, including mean-variance dependency in all experiments. SCRIP also outperformed other methods in recovering cell-cell distances. The application of SCRIP in evaluating differential expression analysis methods showed that edgeR outperformed other examined methods in differential expression analyses, and ZINB-WaVE improved the AUC at high dropout rates. Collectively, this study provides the research community with a rigorous tool for scRNA-seq data simulation. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1093/bioinformatics/btab824 | BIOINFORMATICS |
DocType | Volume | Issue |
Journal | 38 | 5 |
ISSN | Citations | PageRank |
1367-4803 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fei Qin | 1 | 0 | 0.34 |
| Xizhi Luo | 2 | 0 | 1.35 |
| Feifei Xiao | 3 | 0 | 0.68 |
| Guoshuai Cai | 4 | 0 | 0.68 |