estimateDivergence {mia} | R Documentation |
This function estimates a divergence within samples.
estimateDivergence( x, abund_values = "counts", name = "divergence", reference = "median", FUN = vegan::vegdist, method = "bray", ... ) ## S4 method for signature 'SummarizedExperiment' estimateDivergence( x, abund_values = "counts", name = "divergence", reference = "median", FUN = vegan::vegdist, method = "bray", ... )
x |
a |
abund_values |
the name of the assay used for calculation of the sample-wise estimates |
name |
a name for the column of the colData the results should be
stored in. By defaut, |
reference |
a numeric vector that has length equal to number of
features, or a non-empty character value; either 'median' or 'mean'.
|
FUN |
a |
method |
a method that is used to calculate the distance. Method is
passed to the function that is specified by |
... |
optional arguments |
Microbiota divergence (heterogeneity / spread) within a given sample set can be quantified by the average sample dissimilarity or beta diversity with respect to a given reference sample.
This measure is sensitive to sample size. Subsampling or bootstrapping can be applied to equalize sample sizes between comparisons.
x
with additional colData
named *name*
Leo Lahti and Tuomas Borman. Contact: microbiome.github.io
data(GlobalPatterns) tse <- GlobalPatterns # By default, reference is median of all samples. The name of column where results # is "divergence" by default, but it can be specified. tse <- estimateDivergence(tse) # The method that are used to calculate distance in divergence and # reference can be specified. Here, euclidean distance and dist function from # stats package are used. Reference is the first sample. tse <- estimateDivergence(tse, name = "divergence_first_sample", reference = assays(tse)$counts[,1], FUN = stats::dist, method = "euclidean") # Reference can also be median or mean of all samples. # By default, divergence is calculated by using median. Here, mean is used. tse <- estimateDivergence(tse, name = "divergence_average", reference = "mean") # All three divergence results are stored in colData. colData(tse)