Table 28 Overview of method tables with descriptions of statistical methods

1

Methods for visual inspection of univariate and multivariate distributions: Histograms, boxplots, scatterplots, correlograms, heatmaps (Table 2)

2

Methods for descriptive statistics: Measures for location and scale, bivariate measures, RLE plots, MA plots (Table 3)

3

Method for analysis of control values: Calibration curve (Table 4)

4

Methods for graphical displays: Principal component analysis (PCA), Biplot (Table 5)

5

Methods for background subtraction and normalization: Background correction, baseline correction, centering and scaling, quantile normalization (Table 6)

6

Methods for batch correction: ComBat, SVA (surrogate variable analysis) (Table 7)

7

Method for recoding: Collapsing categories (Table 8)

8

Method for filtering and exclusion of variables: Variable filtering (Table 9)

9

Method for construction of new variables: Discretizing continuous variables (Table 10)

10

Method for imputation of missing data: Multiple imputation (Table 11)

11

Methods for graphical displays: Multidimensional scaling, t-SNE, UMAP, neural networks (Table 12)

12

Methods for cluster analysis: Hierarchical clustering, k-means, PAM (Table 13)

13

Methods for estimation of the number of clusters: Scree plots, silhouette values (Table 14)

14

Methods for hypothesis testing for a single variable: T-test, permutation test (Table 15)

15

Methods for hypothesis testing for multiple variables in HDD: Limma, edgeR, DEseq2 (Table 16)

16

Methods for multiple testing corrections: Bonferroni correction, Holm’s procedure, Westfall-Young permutation procedure (Table 18)

17

Methods for multiple testing corrections controlling the FDR: Benjamini-Hochberg, q-values (Table 19)

18

Methods for multiple testing for groups of variables: Gene set enrichment analysis (GSEA), over-representation analysis, global test, topGO (Table 20)

19

Methods for variable transformations: Log-transform, standardization (Table 21)

20

Method for dimension reduction: Supervised principal components (Table 22)

21

Methods for statistical modelling with constraints on regression coefficients: Ridge regression, lasso regression, elastic net, boosting (Table 23)

22

Methods for statistical modelling with machine learning algorithms: Support vector machine, trees, random forests, neural networks and deep learning (Table 24)

23

Methods for assessing performance of prediction models: MSE, MAE, ROC curves, AUC, misclassification rate, Brier score, calibration plots, deviance (Table 25)

24

Methods for validation of prediction models: Subsampling, Cross-validation, Bootstrapping, use of external datasets (Table 26)