If you have problems or questions, ideas for improvements, or even found a bug or two, feel free to post at https://github.com/kasperskytte/ampvis2/issues or email one of the developers, anything and everything is welcome.
By default the raw read counts in the abundance matrix are normalised
(transformed to percentages) by some plotting functions automatically
(for example amp_heatmap, amp_timeseries, and
more). This means that the relative abundances shown will be calculated
based on the remaining taxa after the subset, not including the removed
taxa, if any. To circumvent this, set normalise = TRUE when
subsetting with the amp_subset_taxa and
amp_subset_samples functions, and then set
raw = TRUE in the plotting function. This will transform
the OTU counts to relative abundances BEFORE the subset, and setting
raw = TRUE will skip the transformation in the plotting
function, see the example below.
data("MiDAS")
subsettedData <- amp_subset_samples(MiDAS,
Plant %in% c("Aalborg West", "Aalborg East"),
normalise = TRUE #add this
)
amp_heatmap(subsettedData,
group_by = "Plant",
tax_aggregate = "Phylum",
tax_add = "Genus",
normalise = FALSE #add this
)If you wan’t to calculate a distance matrix manually and use it for
PCoA in amp_ordinate, it can be done quite easily by just
setting filter_species = 0,
transform = "none", and distmeasure = "none",
like below. The matrix should be a symmetrical matrix containing
coefficients for all pairs of samples in the data. This is not ideal for
nMDS (nor possible) since it is not an eigenvalue based method, but if
you really want, set distmeasure = "euclidean" instead of
“none”.
#Override the abundance table in the ampvis2 object with a custom distance matrix
ampvis2_object$abund <- as.dist(custom_dist_matrix)
#set filter_species = 0, transform = "none", and distmeasure = "none"
amp_ordinate(ampvis2_object,
type = "pcoa",
filter_species = 0,
transform = "none",
distmeasure = "none")