Table 1.
Marker gene selection methods benchmarked in this paper
| Package | Version | Language | Parameters | Description | Citation |
|---|---|---|---|---|---|
| Seurat | 4.0.5 | R | test.use = t | Welch’s t-test | [12] |
| - | - | - | test.use = “wilcox” | Wilcoxon rank-sum test (with tie-correction) | - |
| - | - | - | test.use = “LR” | Genewise Logistic regression | - |
| - | - | - | test.use = “negbinom” | Two group Negative Binomial GLM | - |
| - | - | - | test.use = “poisson” | Two group Poisson GLM | - |
| - | - | - | test.use = “roc” | ROC assessment of gene expression as classifier | - |
| - | - | - | test.use = “bimod” | Bimodal likelihood ratio test | -, [27] |
| - | - | - | test.use = “MAST” | MAST | -, [19] |
| COSG | 0.9.0 | R | None | Cosine score | [15] |
| Scanpy | 1.8.1 | Python | test_use = “t-test” rankby_abs = True | Welch t-test ranking by the absolute value of the score | [13] |
| - | - | - | test_use = “t-test_over_estimvar” rankby_abs = True | Welch t-test with overestimated variance ranking by the absolute value of the score | - |
| - | - | - | test_use = “t-test” rankby_abs = False | Welch t-test ranking by the raw score | - |
| - | - | - | test_use = “t-test_over_estimvar” rankby_abs = False | Welch t-test with overestimated variance ranking by the raw score | - |
| - | - | - | test_use = “wilcoxon” rankby_abs = True tie_correct = True | Wilcoxon rank-sum test ranking by absolute value of the score with tie-correction | - |
| - | - | - | test_use = “wilcoxon” rankby_abs = False tie_correct = True | Wilcoxon rank-sum test ranking by the raw score with tie-correction | - |
| - | - | - | test_use = “wilcoxon” rankby_abs = True tie_correct = False | Wilcoxon rank-sum test ranking by absolute value of the score without tie-correction | - |
| - | - | - | test_use = “wilcoxon” rankby_abs = False tie_correct = False | Wilcoxon rank-sum test ranking by the raw score without tie-correction | - |
| scran | 1.22.1 | R | findMarkers() test.type = “t”, pval.type = “any” | Pairwise t-test up-ranking genes with “any” small p-values | [28] |
| - | - | - | findMarkers() test.type = “t”, pval.type = “all” | Pairwise t-test up-ranking genes with “all” small p-values | - |
| - | - | - | findMarkers() test.type = “t”, pval.type = “some” | Pairwise t-test up-ranking genes with “some” small p-values | - |
| - | - | - | findMarkers() test.type = “wilcox”, pval.type = “any” | Pairwise Wilcoxon rank-sum test up-ranking genes with “any” small p-values | - |
| - | - | - | findMarkers() test.type = “wilcox”, pval.type = “all” | Pairwise Wilcoxon rank-sum test up-ranking genes with “all” small p-values | - |
| - | - | - | findMarkers() test.type = “wilcox”, pval.type = “some” | Pairwise Wilcoxon rank-sum test up-ranking genes with “some” small p-values | - |
| - | - | - | findMarkers() test.type = “binom”, pval.type = “any” | Pairwise Binomial test up-ranking genes with “any” small p-values | - |
| - | - | - | findMarkers() test.type = “binom”, pval.type = “all” | Pairwise Binomial test up-ranking genes with “all” small p-values | - |
| - | - | - | findMarkers() test.type = “binom”, pval.type = “some” | Pairwise Binomial test up-ranking genes with “some” small p-values | - |
| scran | 1.22.1 | R | scoreMarkers(), mean.logFC.cohen | Pairwise Cohen’s d, mean across genes | NA |
| - | - | - | scoreMarkers(), min.logFC.cohen | Pairwise Cohen’s d, minimum across genes | - |
| - | - | - | scoreMarkers(), median.logFC.cohen | Pairwise Cohen’s d, median across genes | - |
| - | - | - | scoreMarkers(), max.logFC.cohen | Pairwise Cohen’s d, maximum across genes | |
| - | - | - | scoreMarkers(), rank.logFC.cohen | Pairwise Cohen’s d, min rank across genes | - |
| - | - | - | scoreMarkers(), mean.AUC | Pairwise AUC, mean across genes | - |
| - | - | - | scoreMarkers(), median.AUC | Pairwise AUC, median across genes | - |
| - | - | - | scoreMarkers(), min.AUC | Pairwise AUC, minimum across genes | - |
| - | - | - | scoreMarkers(), max.AUC | Pairwise AUC, maximum across genes | - |
| - | - | - | scoreMarkers(), rank.AUC | Pairwise AUC, min rank across genes | - |
| - | - | - | scoreMarkers(), mean.logFC.detected | Pairwise lfc in detection proportion, mean across genes | - |
| - | - | - | scoreMarkers(), min.logFC.detected | Pairwise lfc in detection proportion, minimum across genes | - |
| - | - | - | scoreMarkers(), median.logFC.detected | Pairwise lfc in detection proportion, median across genes | - |
| - | - | - | scoreMarkers(), max.logFC.detected | Pairwise lfc in detection proportion, maximum across genes | - |
| - | - | - | scoreMarkers(), rank.logFC.detected | Pairwise lfc in detection proportion, min rank across genes | - |
| presto | 1.0.0 | R | None | Optimized Wilcoxon rank-sum test | NA |
| edger | 3.36.0 | R | GLM | Negative Binomial GLM with empirical Bayes (EB) shrinkage | [29, 30] |
| edger | 3.36.0 | R | QL | - With quasi-likelihood fitting | [29, 30] |
| RankCorr | NA | Python | lambda = 2 | Sparse seperating hyperplane selection | [26] |
| RankCorr | NA | Python | lambda = 5 | - | [26] |
| RankCorr | NA | Python | lambda = 10 | - | [26] |
| glmGamPoi | 1.6.0 | R | None | Negative Binomial QL GLM with EB shrinkage | [31] |
| limma | 3.50.0 | R | Standard | Linear model with EB shrinkage | [32, 33] |
| limma | 3.50.0 | R | Voom | - With weighting | [32, 33] |
| limma | 3.50.0 | R | Trend | - | [32, 33] |
| Cepo | 1.0.0 | R | None | Stability statistic | [34] |
| NSForest | 3.0 | Python | None | Random forest classifier | [14] |
| Venice | 0.0.11 | R | None | Classification scoring | [35] |
| SMaSH | 0.1.2 | Python | None | Deep neural net classifier | [7] |