| Upload |
Data upload |
| Data |
Information on uploaded data |
| QViz |
Quantitative visualization of data as heatmap, barchart or bubble plots. Measurements are visualized for individual samples. |
| GroupPlots |
Comparison of measurements across multiple biological conditions (e.g. using ANOVA or non-parametric rank tests). Features that are significantly differentially distributed are presented as boxplots or barcharts. |
| Features |
Quantitative visualization of measurements obtained for individual features. |
| Stats |
Comparison of measurements across multiple biological conditions (e.g. using ANOVA, non-parametric rank tests, Bayesian ANOVA or DESeq2). P-values are adjusted for multiple testing and results are presented as table. |
| Multivariate |
Multivariate data analysis. Various methods are available for data ordination (e.g. PCA, PCoA, NMDS) and multivariate statistical testing (e.g. CCA, RDA). |
| Feat. Select |
Feature selection using stepwise regression, LASSO regression, random forest or LEfSe |
| Network |
Network analysis and clustering using self organizing maps |
| Biomarker |
Identification of biomarker candidates. Biomarkers are characterized by Area Under the Curve (AUC), fold change and delta (difference in mean in units of standard deviation). Results are presented as table and forest plot. |
| Regression |
Identification of associations between measurements and multiple explanatory variables using multivariable regression techniques |
| Rep. Measures |
Analysis of experimental data from repeated measures designs using mixed effect regression models. This technique can distinguish between group-specific effects and subject or cage-specific effects. |
| Paired |
Analysis of paired data using paired t-test or paired rank test |
| Norm |
Visualize the effect of different data transformation and data normalization methods |
| FactorAnalysis |
Factor analysis to reduce data dimensionality and remove redundant features |
