Practical-3-Processing-16S-rRNA-amplicon-data.md

@@ -160,48 +160,48 @@ Now we are going to use the produced files in order to make some plots and visua
 
 First, let's load the necessary libraries:
 
-    library(phyloseq)
-    library(ggplot2)
-    library(reshape2)
-    library(RColorBrewer)
-    library(vegan)
-    library(plyr)
+    > library(phyloseq)
+    > library(ggplot2)
+    > library(reshape2)
+    > library(RColorBrewer)
+    > library(vegan)
+    > library(plyr)
 
 Before we create our phyloseq object, we can import some metadata about our samples, e.g. sample type, fraction etc.
 
-    sample_metadata <- read.table("sample_metadata.csv", header=TRUE, sep="\t", stringsAsFactors=FALSE)
-    rownames(sample_metadata) <- sample_metadata$Sample
-    View(sample_metadata)
+    > sample_metadata <- read.table("sample_metadata.csv", header=TRUE, sep="\t", stringsAsFactors=FALSE)
+    > rownames(sample_metadata) <- sample_metadata$Sample
+    > View(sample_metadata)
 
 Now let's create our phyloseq object
 
-    physeq_marmic <- phyloseq(otu_table(asv_count_tab, taxa_are_rows=TRUE), 
+    > physeq_marmic <- phyloseq(otu_table(asv_count_tab, taxa_are_rows=TRUE), 
                         tax_table(taxa_with_species), sample_data(sample_metadata))
 
 Once you have created your phyloseq object, the datasets can be accessed by using the following syntax
 
-    otu_table(physeq_marmic)
-    tax_table(physeq_marmic)
-    sample_data(physeq_marmic)
+    > otu_table(physeq_marmic)
+    > tax_table(physeq_marmic)
+    > sample_data(physeq_marmic)
 
 Make some basic diversity plots and rarefaction curves
 
     ## Diversity plots
-    alpha_plot_marmic <- plot_richness(physeq_marmic, measures=c("Chao1", "Shannon", "Simpson"), x="sample", nrow=3)
-    alpha_plot_marmic
+    > alpha_plot_marmic <- plot_richness(physeq_marmic, measures=c("Chao1", "Shannon", "Simpson"), x="sample", nrow=3)
+    > alpha_plot_marmic
 
     ## Rarefaction curves to provide indication of the coverage of our community
-    rarefaction_marmic <- rarecurve(t(otu_table(physeq_marmic)), step=50, cex=0.5)
-    rarefaction_marmic
+    > rarefaction_marmic <- rarecurve(t(otu_table(physeq_marmic)), step=50, cex=0.5)
+    > rarefaction_marmic
 
 Now let's visualise the composition of our sample communities using basic barplots. For this, we first need to calculate relative abundance
 
-    physeq_marmic_rel <- transform_sample_counts(physeq_marmic, function(otu) otu/sum(otu))
-    physeq_marmic_rel
+    > physeq_marmic_rel <- transform_sample_counts(physeq_marmic, function(otu) otu/sum(otu))
+    > physeq_marmic_rel
 
 First let's plot phylum-level composition
 
-    marmic_phylum_barplot <- plot_bar(physeq_marmic_rel, fill="Phylum") +
+    > marmic_phylum_barplot <- plot_bar(physeq_marmic_rel, fill="Phylum") +
     facet_grid(~Sample_group, scales="free_x") + 
     guides(fill=guide_legend(ncol=1)) + 
     labs(y="Relative abundance", x="Sample") + 
@@ -212,11 +212,11 @@ First let's plot phylum-level composition
         legend.title = element_text(size=12,face="bold",colour="black"),
         strip.background.x = element_rect(fill="white"), 
         strip.text.x = element_text(size=11,face="bold",colour="black"))
-    herschel_phylum_barplot
+    > marmic_phylum_barplot
 
 Now at the Family level
 
-    marmic_family_barplot <- plot_bar(physeq_marmic_rel, fill="Family") +
+    > marmic_family_barplot <- plot_bar(physeq_marmic_rel, fill="Family") +
     facet_grid(~Sample_group, scales="free_x") + 
     guides(fill=guide_legend(ncol=1)) + 
     labs(y="Relative abundance", x="Sample") +