View source: R/plotConcordance.R
plotConcordance | R Documentation |
Produce a list of graphical outputs summarizing the between and within method concordance.
plotConcordance(concordance, threshold = NULL, cols = NULL)
concordance |
A long format |
threshold |
The threshold for rank (x-axis upper limit if all methods have a higher number of computed statistics). |
cols |
A named vector containing the color hex codes. |
A 2 elements list of ggplot2
class objects:
concordanceDendrogram
which contains the
vertically directioned dendrogram for the methods involved in the
concordance analysis;
concordanceHeatmap
which contains the heatmap of between
and within method concordances.
createConcordance
data(ps_plaque_16S) # Balanced design my_splits <- createSplits( object = ps_plaque_16S, varName = "HMP_BODY_SUBSITE", balanced = TRUE, paired = "RSID", N = 10 # N = 100 suggested ) # Make sure the subject ID variable is a factor phyloseq::sample_data(ps_plaque_16S)[, "RSID"] <- as.factor( phyloseq::sample_data(ps_plaque_16S)[["RSID"]]) # Initialize some limma based methods my_limma <- set_limma(design = ~ RSID + HMP_BODY_SUBSITE, coef = "HMP_BODY_SUBSITESupragingival Plaque", norm = c("TMM", "CSS")) # Set the normalization methods according to the DA methods my_norm <- setNormalizations(fun = c("norm_edgeR", "norm_CSS"), method = c("TMM", "CSS")) # Run methods on split datasets results <- runSplits(split_list = my_splits, method_list = my_limma, normalization_list = my_norm, object = ps_plaque_16S) # Concordance for p-values concordance_pvalues <- createConcordance( object = results, slot = "pValMat", colName = "rawP", type = "pvalue" ) # plot concordances from rank 1 to 50. plotConcordance( concordance = concordance_pvalues, threshold = 50 )
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