From: Beyond genomics: understanding exposotypes through metabolomics
Class of test | Type of test | Application/description | Refs |
---|---|---|---|
Descriptive | Mean Median Mode | The simplest of tests used to describe basic features within data. | Covered in all general statistical textbooks and used in most if not all scientific disciplines. |
Range, variance, SD | Describe spreads of data within a population | ||
Inferential | z test, t test, chi-square | Predicts/infers an observed mean, frequency, or proportion to a predetermined value, respectively. | |
ANOVA | Parametric method that tests the hypothesis that the means of two or more populations are equal. Frequently used to compare variance among groups relative to variance within groups | ||
Kruskal-Wallis | Non-parametric method to rank statistical significant differences between two or more groups of an independent variable on a continuous/ordinal variable | ||
Scaling | Centering, auto, pareto, log, MD | Data pretreatment methods aim at reducing biological and analytical bias | |
Principal component | PCA | Unsupervised dimensional reduction procedure used to explain the maximum variance within complex datasets. | |
Multiblock PCA | PCA extension designed to find the underlying relationships between sets of related data | ||
ANOVA-PCA | Uses PC dimensional reduction to determines the effect of the experimental factors on multiple dependent variables | ||
PC-DFA | Supervised test that summarizes the differentiation between groups while overlooking within-group variation. | ||
Regression | Linear | Summarizes and quantifies the relationship between two continuous variables | |
 | PLS | Used to predict a set of dependent variables from a large set of independent variables | |
O-PLS | orthogonal signal correction on PLS that maximizes the explained covariance on the first latent variable | ||
PLS-R | Combination of the predictive power of regression alongside the ability to deal with high dimensionality and multicollinearity of variables. | ||
PLS-DA | Supervised approach to prediction on discrete variables | ||
LASSO | Parsimonious approach to variable selection and regularization in order to enhance interpretability and reduce noise | ||
Elastic net | Variable reduction approach where strongly correlated predictors coalesce in or out of the model together |