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excludeAdditional Assistance

The Non-parametric ANOVA task uses the Kruskal-Wallis and Dunn's tests (Non-parametric ANOVA) task is used to identify deferentially expressed genes among two or more groups. Note that such rank-based tests are generally advised for use with larger sample sizes.

Running the task

To invoke the NonKruskal-parametric ANOVAWallis test, select any count-based data nodes, these include:

  • Gene counts
  • Transcript counts 
  • Normalized counts  

Select Non-parametric ANOVA under the Statistical analysis section of the context Statistics > Differential analysis in the context-sensitive menu, then select Kruskal-Wallis (Figure 1). 

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SubtitleTextSelect any count node to invoke the Non-parametric ANOVA task
AnchorNameNon-parametric ANOVA


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 Select a specific factor for analysis and click the Next button (Figure 2). Note that this task can only take into account one factor at a time.

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SubtitleTextSelect one factor for analysis

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 For more complicated experimental designs, go back to the original count data that will be used as input and perform Rank normalization at the Features level (Figure 3). The resulting Normalized counts data node can then be analyzed using the Detect differential expression (ANOVA) task, which can take into account multiple factors as well as interactions.

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SubtitleTextNormalize your count data by rank to do non-parametric testing on more complicated experimental designs

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 Define the desired comparisons between groups and click the Finish button (Figure 4). Note that comparisons can only be added between single group (i.e. one group per box). 

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SubtitleTextSet-up desired comparisons

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The results of the analysis will appear similar to other differential expression analysis results. However, the column to indicate mean expression levels for each group will display the median instead (Figure 5).

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SubtitleTextThe task's ANOVA report will display the median instead of the LSmean

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Additional assistance


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