Name
Title | ||
|---|---|---|
Comparative analysis of differential gene expression analysis tools for single-cell RNA sequencing data. |
Abstract | ||
|---|---|---|
In general, agreement among the tools in calling DE genes is not high. There is a trade-off between true-positive rates and the precision of calling DE genes. Methods with higher true positive rates tend to show low precision due to their introducing false positives, whereas methods with high precision show low true positive rates due to identifying few DE genes. We observed that current methods designed for scRNAseq data do not tend to show better performance compared to methods designed for bulk RNAseq data. Data multimodality and abundance of zero read counts are the main characteristics of scRNAseq data, which play important roles in the performance of differential gene expression analysis methods and need to be considered in terms of the development of new methods. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1186/s12859-019-2599-6 | BMC bioinformatics |
Keywords | Field | DocType |
Comparative analysis,Differential gene expression analysis,RNAseq,Single-cell | RNA,Gene,Biology,Gene expression,Software,Computational biology,Accuracy and precision,Genetics,Sample size determination,DNA microarray,False positive paradox | Journal |
Volume | Issue | ISSN |
20 | 1 | 1471-2105 |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tianyu Wang | 1 | 120 | 30.07 |
| Boyang Li | 2 | 82 | 12.61 |
| Craig E Nelson | 3 | 0 | 0.34 |
| Sheida Nabavi | 4 | 18 | 8.68 |