...
Combined Shannon and Observed ASV. An additional plot combing both Shannon’s index and Observed ASV indices has been included, to compare similarities and differences between these results. As each index uses different units, results for both have been normalised between 0 and 1.
Kruskal-Wallis rank sum test is a rank-based nonparametric test that can be used to determine if there are statistically significant differences between two or more groups. This statistical analysis is provided for each plot, to estimate if there is a significant difference (q.value < 0.05) between all groups.
Pairwise Wilcoxon rank sum test (AKA: Mann-Whitney test is the same as the Kruskal-Wallis test, but applied pairwise to each group (technically, The Kruskal-Wallis test is the generalization of the Wilcoxon rank sum test).
3. Preparing your data
Import the samples table
When you ran your sequences through the ampliseq pipeline, you submitted the samples with a metadata file. This file contains information on your samples and variables. We need to import this metadata file to run our analysis on selected variables.
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samples_table <- read.table("metadata.tsv", sep = "\t", header = T) |
Have a look at your samples table and variables (metadata):
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samples_table |
Import the ASV abundance table
First import the unfiltered ASV table (ampvis2 does internal filtering).
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asvtable <- read.table("./results/abundance_table/unfiltered/feature-table.tsv", check.names = FALSE, sep = "\t", skip = 1, stringsAsFactors = FALSE, comment.char = "", header = T)
colnames(asvtable)[1] <- "ASV" |
Have a look at the top few rows of the ASV table, to see if it looks right. The sample IDs should be the column names and the ASV IDs in the first column. All the other columns should contain numbers (i.e. the count of the number of times each ASV was found in each sample).
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head(asvtable) |
Important: always run this next code cell, even if you haven't subset your data If you're working with a subset of your data (see section 3) you'll need to choose a name for your subgroup (e.g. subgroup <- "high_fat") or if you haven't subset your data (i.e. you're working with all your samples) subgroup <- "all" . This is for naming output plots and files by subset group, so you're not overwriting them each time you run a subset of your data.
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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.
Read in the taxonomy data - i.e. the taxonomic assignments for each ASV
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mytax <- read.table("./results/taxonomy/taxonomy.tsv", sep = "\t", header = T, quote = "") |
Each taxonomic level needs to be in a separate column, and in taxonomy.tsv they are all combined and separated by ';' so we need to split by that delimiter. Use tidyr::separate function for this.
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mytaxsplit <- tidyr::separate(data = mytax, col = Taxon, into = c("Kingdom", "Phylum", "Class", "Order", "Family", "Genus", "Species"), sep = ";") |