Table 2.
A table with information about different software tools for scRNA-seq power analysis with two distinct detection targets. Experimental Factors: cell number (1), individual number (2), Sequencing depth (3).
| Detection Target | # of Samples |
Tool Name | Experimental Factor | Software | Model | Power Assessment |
|---|---|---|---|---|---|---|
| Cell sub- population |
Single sample | ‘SCOPIT’ [37] | (1) | R package & Web application |
Multinomial | Analytical |
| ‘howmanycells’ | Web application | Negative Binomial | ||||
| Multi sample | ‘Sensei‘ [38] | (1), (2) | Beta Binomial | |||
| ‘scPOST’ [39] | R package | Linear mixed model | Simulation- based |
|||
| DEG | ‘scPower’ [40] | (1), (2), (3) | R package & Web server |
Negative Binomial | Pseudobulk | |
| ‘hierarchicell’ [41] | R package | Simulation- based |
||||
| Single sample | ‘powsimR’ [42] | (1) | ||||
| ‘POWSC’ [43] | (1), (3) | A mixture of zero-inflated Poisson and log-normal Poisson distributions | ||||
| ‘scDesign’ [44] | Gamma-Normal mixture model |