| Covariate Name | Best score | Up/Flat, Up/Down, Flat/Down |
| name | scores | scores |
| Covariate Name | Best score |
| name | score |
For a given principal component PCn, display a score for each condition (e.g. tissue or sample) covariate indicating the degree to which that covariate is correlated with that PCn's significant condition grouping into Up/Flat/Down conditions. A score will be generated for each user-supplied column labeling attached to a dataset (i.e. all of the dataset's covariate annotations, e.g. age, tissue, treatment). For discrete covariates a normalized mutual information (NMI) score is computed, indicating the degree agreement between the covariate's discrete values and the condition Up/Flat/Down grouping (a higher score means better agreement). For continuous covariates, Wilcoxon rank sum tests are generated, giving the likelihood of 2 sets of covariate values belonging to the same distribution. Three pairs of condition groups are scored: (Up vs. Flat), (Up vs. Down) and (Flat vs. Down).
Use the choose component to see which principal component you want to explore. The format is principal component number - percent variance captured.
The list of check boxes is used to select which annotation you would like to see reported in your list.