Kruskal-Wallis / Wilcoxon

Both Kruskal-Wallis and Wilcoxon tests are rank tests, such rank-based tests are generally advised for use with larger sample sizes. They both can only take one factor into account at a time. Kruskal-Wallis can perform on an attribute with two or more subgroups.

Wilcoxon test is a close alternative to Kruskal-wallis task, match the results of scany Wilcoxon method. This test is also called "Wilcoxon Rank-Sum Test" or "Mann-Whitney U Test". When you perform comparisons on the two groups, it will filter only include the two groups first and then perform the differential analysis.

Running the task

To invoke the Kruskal-Wallis test, select any count-based data nodes, these include:

• Gene counts

• Transcript counts

• Normalized counts

After clicking on the chosen node:

• Select Statistics > Differential analysis in the context-sensitive menu.

• Select Kruskal-Wallis or Wilcoxon.

• Select a specific factor for analysis and click the Next button to setup the comparisons.

• Define the comparisons by dragging and dropping each group in the Denominator and Numerator boxes and click Add comparison

If the data has not been filtered upstream the Low value filter box will be checked by default. Similarly, the Default normalisation will be selected if the software detects that the data as not been previously normalised.

Advanced option

If there are tied ranks of feature expression values, the default is not use tie correction which is corresponding to the scanpy.tl.rank_genes_groups(tie_correct = False).

The results of the analysis will appear similar to other ANOVA/LIMMA-trend/LIMMA-voom. However, the column to indicate mean expression levels for each group will display the median instead for Kruskal-Wallis.