Description Usage Arguments Value Author(s) References Examples
This function finds the features that are significantly differentially abundant in the provided taxa abundance data under different conditions using Kruskal-Wallis test. The p-values values generated are corrected for multiple testing using family wise error rate. Significance is based on the corrected pvalue threshold. Significant features are assigned importance using random forest classifier. The measure of importance used in this case is mean decrese accuracy.
1 | kruskal_abundance(physeq, grouping_column, pvalue.threshold = 0.05)
|
physeq |
(Required). A |
grouping_column |
(Required). Character string specifying name of a categorical variable that is preffered for grouping the information. information, this should be one of the components of grouping vector. |
pvalue.threshold. |
Cut off p-value for significance of differentially abundant taxa, default is 0.05. |
Returns a list of three items:
SignfeaturesTable: A data.frame
of taxa with coresponding raw p-values, corrected p-values, family wise error rate and expected abundance
computed by using raw p-values.
importance: A data.frame
of taxa mean decrease in accuracy as obtained by random forest classifier.
plotdata: A data.frame
of taxa and corresponding corrected p-values, importance rank organised
in form accepted by ggplot.
Alfred Ssekagiri assekagiri@gmail.com, Umer Zeeshan Ijaz Umer.Ijaz@glasgow.ac.uk
http://userweb.eng.gla.ac.uk/umer.ijaz/, Umer Ijaz, 2015
1 2 3 4 5 6 | data(pitlatrine)
physeq <- pitlatrine
physeq <- taxa_level(physeq,"Phylum")
kw_sig <- kruskal_abundance(physeq, "Country")
#plot the significant features
plot_signif(kw_sig$plotdata) #see function \link[microbiomeSeq]{plot_signif}
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.