Table 1 Overview of the structure of the paper, as a list of the sections with corresponding analytical goals and common approaches
Section | Analytical goals | Common approaches | Examples |
|---|---|---|---|
IDA | Initial data analysis and preprocessing | ||
IDA1 | Identify inconsistent, suspicious or unexpected values | Visual inspection of univariate and multivariate distributions | Histograms, boxplots, scatterplots, correlograms, heatmaps |
IDA2 | Describe distributions of variables, and identify missing values and systematic effects due to data acquisition | Descriptive statistics, tabulation, analysis of control values, graphical displays | Measures for location and scale, bivariate measures, RLE plots, MA plots, calibration curve, PCA, Biplot |
IDA3 | Preprocess the data | Normalization, batch correction | Background correction, baseline correction, centering and scaling, quantile normalization, ComBat, SVA |
IDA4 | Simplify data and refine/update analysis plan if required | Recoding, variable filtering and exclusion of uninformative variables, construction of new variables, removal of variables or observations due to missing values, imputation | Collapsing categories, variable filtering, discretizing continuous variables, multiple imputation |
EDA | Exploratory data analysis | ||
EDA1 | Identify interesting data characteristics | Graphical displays, descriptive univariate and multivariate statistics | PCA, Biplot, multidimensional scaling, t-SNE, UMAP, neural networks |
EDA2 | Gain insight into the data structure | Cluster analysis, prototypical samples | Hierarchical clustering, k-means, PAM, scree plot, silhouette values |
TEST | Identification of informative variables and multiple testing | ||
TEST1 | Identify variables informative for an outcome | Test statistics, modelling approaches | t-test, permutation test, limma, edgeR, DESeq2 |
TEST2 | Perform multiple testing | Multiple tests, control for false discoveries | Bonferroni correction, Holm’s procedure, multivariate permutation tests, Benjamini-Hochberg (BH), q-values |
TEST3 | Identify informative groups of variables | Tests for groups of variables | Gene set enrichment analysis, over-representation analysis, global test, topGO |
PRED | Prediction | ||
PRED1 | Construct prediction models | Variable transformations, variable selection, dimension reduction, statistical modelling, algorithms, integrating multiple sources of information | Log-transform, standardization, superPC, ridge regression, lasso regression, elastic net, boosting, SVM, trees, random forest, neural networks, deep learning |
PRED2 | Assess performance and validate prediction models | Choice of performance measures, internal and external validation, identification of influential points | MSE, MAE, ROC curves, AUC, misclassification rate, Brier score, calibration plots, deviance, subsampling, cross-validation, bootstrap, use of external datasets |