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

Select Statistics > Differential analysis in the context-sensitive menu, then select Kruskal-Wallis or Wilcoxon. Select a specific factor for analysis and click the Next button to setup the comparisons.

Note: Wilcoxon test will filter the data to include the observations in the two comparison groups to generate p-value, while Kruskal-Wallis will use all the samples in the input data to generate p-value on the selected attribute.

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.