Table 1.
Comparative overview of the DEA approaches for scRNA-seq data analysis.
| SN. | Methods | Year | Model | Input | DE Test Stat. | Runtime | Platform | Ref. |
|---|---|---|---|---|---|---|---|---|
| 1 | NBID | 2018 | NB (GLM) | Counts | LRT | Medium | R code | [30] |
| 2 | ZINB–WaVE | 2018 | ZINB (GLM) | Counts | LRT | High | Bioconductor, GitHub | [31] |
| 3 | zingeR | 2018 | ZINB (GLM) | Counts | LRT | High | GitHub | [32,33] |
| 4 | DECENT | 2019 | ZINB (GLM) | Counts | LRT | High | GitHub | [24] |
| 5 | SwarnSeq | 2021 | ZINB (GLM) | Counts | LRT | High | GitHub | [13] |
| 6 | Tweedieverse | 2021 | ZITweedie (GLM) | Counts | Wald | High | GitHub | [34] |
| 7 | scMMST | 2021 | GLMM | Counts | Norm. score | High | NA | [35] |
| 8 | TPMM | 2022 | GLMM | Norm. | Wald/LRT | High | GitHub | [36] |
| 9 | Monocle2 | 2017 | GAM | Norm. | LRT | Medium | Bioconductor | [37,38] |
| 10 | tradeSeq | 2020 | GAM | Counts | Wald | Medium | GitHub | [39] |
| 11 | MAST | 2015 | Hurdle | Norm. | LRT/Wald | Medium | Bioconductor | [40] |
| 12 | Random-Hurdle | 2019 | Hurdle | Counts | Chi-square test statistic | High | NA | [41] |
| 13 | SCDE | 2014 | Poisson-NB (MM) | Counts | Bayesian stat. | High | Bioconductor | [42] |
| 14 | BASiCS | 2015 | Poisson-Gamma (MM) | Norm. | Posterior prob. | High | Bioconductor | [25] |
| 15 | D3E | 2016 | Poisson-Beta (MM) | Counts | CM/KS test | High | GitHub | [43] |
| 16 | BPSC | 2016 | Beta-Poisson (MM) | Counts | LRT | Medium | GitHub | [12] |
| 17 | TASC | 2017 | Logistic, Poisson Models (MM) | UMI | LRT | High | GitHub | [26] |
| 18 | DESCEND | 2018 | Poisson-Alpha (MM) | Counts | Normalized Gini Score | High | GitHub | [28] |
| 19 | SC2P | 2018 | ZIP, Poisson-Lognormal (MM) | Counts | Posterior prob. | High | GitHub | [44] |
| 20 | ZIAQ | 2020 | Logistic and quantile Regression (MM) | Norm. | Fisher’s test | Medium | GitHub | [45] |
| 21 | SimCD | 2021 | Gamma-NB (MM) | Counts | Bayesian | High | GitHub | [46] |
| 22 | ZIQRank | 2022 | Zero-inflated model, quantile regression (MM) | Cont. | Rank-score test | High | NA | [47] |
| 23 | Seurat | 2015 | NB (TCP) | Counts | LRT | Low | CRAN | [48,49] |
| 24 | scDD | 2016 | Multi-modal Bayesian (TCP) | Norm. | Bayesian stat. | High | Bioconductor | [50] |
| 25 | DEsingle | 2018 | ZINB (TCP) | Counts | LRT | High | Bioconductor, GitHub | [51] |
| 26 | NYMP | 2019 | Logistic regression (TCP) | Cont. | Medium | GitHub | [52] | |
| 27 | t-test | logCPM (TCP) | Norm. | T stat | Low | CRAN | [10] | |
| 28 | IDEAS | 2022 | NB/ZINB/Kernel Density estimation/ Cumulative distribution function (TCP) |
Counts/Cont. | Jensen–Shannon Divergence/ Wasserstein distance |
High | GitHub | [53] |
| 29 | SAMstrt | 2013 | NP | Counts | Medium | GitHub | [54] | |
| 30 | Wilcox | NP | Counts/Norm. | Sum ranks | Low | CRAN | [10] | |
| 31 | SINCERA | 2015 | NP | Norm. | Welch (LS)/ Wilcox (SS) |
High | GitHub | [55] |
| 32 | NODES | 2016 | NP | Norm. | Wilcox | Medium | Dropbox | [56] |
| 33 | EMDomics | 2016 | NP | Norm. | Euclidean distance | High | Bioconductor | [57] |
| 34 | sigEMD | 2018 | NP | Norm. | Distance measure | High | GitHub | [58] |
| 35 | DTWscore | 2017 | NP | FPKM | Distance | Medium | GitHub | [59] |
| 36 | ROSeq | 2021 | NP | Counts/Norm. | Wald | High | Bioconductor, GitHub | [60] |
| 37 | scDEA 1 | 2021 | 12 Models (Hybrid) | Counts | Lancaster’s test (Chi) | High | GitHub | [61] |
CM: Cramér–von Mises test; Counts: read/UMI counts; Cont.: continuous values, e.g., FPKM, log(CPM), RPKM; NA: source codes are not freely available; Norm.: normalized; GLM: generalized linear model; NB: negative binomial; GLMM: generalized linear mixed model; NP: non-parametric; GAM: generalized additive model; MM: mixture model; TCP: two-class parametric. 1: Integrated approach.