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The ampvis2 package requires a 3 components:

1. A table of read counts per sample per ASV (abundance table)

2. A table that matches ASVs to taxonomy information

3. A metadata table containing your sample IDs and any variables (e.g. experimental groups) associated with each sample.

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nfcore/ampliseq generates the abundance and taxonomy tables, and we created the metadata table in the previous section, so we can import all 3 into R.

Import the metadata table

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subgroup <- "all"

Convert the imported data to ampvis2 format

The following code cell manipulates the data in a variety of ways (see the in-code #comments for explanations) to prepare the data for conversion to an ampliseq2 database.

Import the taxonomy table

Read in the taxonomy data - i.e. the taxonomic assignments for each ASV

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asv_table$Kingdom <- gsub("D_0", "k", asv_table$Kingdom)
asv_table$Phylum <- gsub("D_1", "p", asv_table$Phylum)
asv_table$Class <- gsub("D_2", "c", asv_table$Class)
asv_table$Order <- gsub("D_3", "o", asv_table$Order)
asv_table$Family <- gsub("D_4", "f", asv_table$Family)
asv_table$Genus <- gsub("D_5", "g", asv_table$Genus)
asv_table$Species <- gsub("D_6", "s", asv_table$Species)

Convert the imported data to ampvis2 format

Now combine the samples data with the ASV table using amp_load(). This creates an ampvis2 database that can be used by ampvis2

ampvisdata <- amp_load(otutable = asv_table,
              metadata = samples_table)

You can see information about the ampvis2 object you just created by typing its name

ampvisdata

4b. Choosing a categorical variable to analyse

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