Table 1 Overview of the data sets and the methods used in this study
Data set (D) | Classes* | No. of genes** |
|---|---|---|
Alizadeh | DLBCL (68), other samples (65) | 7806 (7430) |
Finak | Epithelial (34), stromal tissue (32) | 33491 |
Galland | Invasive NFPAs (22), non- invasive NFPAs (18) | 40475 (40291) |
Herschkowitz | High ER expression (58), low ER expression (46) | 19718 |
Jones | Cancerous samples (72), non-cancerous samples (19) | 40233 (39746) |
Sørlie | High ER expression (55), low ER expression (18) | 8033 (7734) |
Ye | Metastatic (65), non-metastatic (22) | 8911 |
Normalization (No) | Description | |
No 0 | Raw data | |
No 1 | Print-tip MA-loess, no background correction | |
No 2 | Print-tip MA-loess, background correction | |
No 3 | Global MA-loess, no background correction | |
No 4 | Global MA-loess, background correction | |
Gene selection (G) | Fixed parameters | |
T-test | Two-sided | |
Relief | Threshold = 0, nosample = # obs. in data set | |
Paired distance | Euclidian distance | |
Number of genes (N) | 2, 12, 22, 32, 42, 52, 62, 72, 82, 92, 100, 200, 300, 400, 150, 500, 600, 700, 800, 900, 1000 | |
Machine learning (M) | Description, Fixed parameters | Optimized parameters |
DT Gini | Decision tree, Splitting index = Gini | |
DT Information | Decision tree, Splitting index = Information | |
NN One layer | Neural Network, one hidden layer, decay = 0.001, rang = 0.1, maxit = 100 | size = [2-5] |
NN No layer | Neural Network, no hidden layer, decay = 0.001, rang = 0.1, maxit = 100, skip = TRUE, size = 0 | |
SVM Linear | Support Vector Machine, linear kernel, type = nu-scv, cross = 10, nu = 0.2, scaled = FALSE | |
SVM Poly2 | Support Vector Machine, polynomial kernel, deg 2, type = nu-scv, cross = 10, nu = 0.2, scaled = FALSE | |
SVM Poly3 | Support Vector Machine, polynomial kernel, deg 3, type = nu-scv, cross = 10, nu = 0.2, scaled = FALSE | |
SVM Rb | Support Vector Machine, radial basis kernel, type = nu-scv, cross = 10, nu = 0.2, scaled = FALSE | sigma = [2-14, 214] |