Interpretation of Results

Statistical Comparison Result-Table

Cross-sectional Comparison

_images/resulttable.png

The index (rows) of the result table consists of the feature name on the outer level and the dataset combination used in the respective comparison as second level index. FEATURE1 (1, 2) hereby declares that this row represents the comparison of FEATURE1 between dataset 1 and 2,where 1 and 2 point to the position of the respective dataset in the DataCollection.

The result table of a feature comparison lists in the columns:

  • The p-values
  • The familywise error corrected p-values
  • A boolean indicator if the test result was significant (a significance level alpha of 0.05 is assumed)
  • One column per dataset listing the number of observations available for the comparison of that feature
_images/sig_feat_example.png

The above figure shows the visualization of data as a boxplot (A) and a comparative kernel density estimate plot (B).

Longitudinal Comparison

_images/longresult.png

Like for the cross-sectional comparison the table is indexed using multiple layers. The first and third index refer again to the feature name and the datasets being compared, respectively. The second layer gives information on the longitudinal dimension. It lists the longitudinal value (e.g. a time point) at which the corresponding statistical tests have been performed.

_images/FEAT1prog_score_ROBL.png

This figure depicts a visualization of the progression of a feature over multiple longitudinal steps.

Clustering

The confusion matrix of the hierarchical clustering lists the datasets as rows and clusters as columns. Each cell represents the number of entities of that dataset being assigned to the respective cluster. Based on the contingency matrix a chi² test is performed to assess if the distribution across clusters is significantlydifferent for the datasets. If so, there might be systematic batch effects present.