In microsud/microbiomeutilities: microbiomeutilities: Utilities for Microbiome Analytics

Microbiome analytics template

This template can be used as a guide for analysing marker-gene sequecing data. The basic steps after processing of raw reads include checking the quality of the OTU Tables, .biom files, checking the sequencing depth for each samples, if there is large difference in the sequencing depth then the either rarefying to equal library sizes of converting it raw counts to proportions may be required. Alpha diversity, beta diversity calculations are very commonly done.

This template is for microbiome data analysis, handling and visualisation. This makes it convinient to follow some standard procedures for looking at your data and reporting your results. This first-hand look at your data will aid in making different choices for more thorough and in-dept investigation.

There are several ways to organise data. Check this link on Organize your data and code.

You can find the source code at microbiomeutilities github repo.

A step wise guide can be found at microbiomeutilities wiki.

Set project attributes

Fill the details accordingly.

Project title : Create a standard workflow for data reporting.
Main author : Yourname
contributor(s) : XXXX
Principal Investigator (s) : XXXX
Co-supervisor(s) : XXXXX

The next code chunk is where you put all the parameters for analysis. This is the basis for your prelimanry analysis.
We assume that you have created an Rproject for the analysis. Firstly, few folders have to be created within the Rproject.

# Store QC results 
dir.create("QC")

# Store alpha diversity results  
dir.create("AlphaDiversity")

# you can also create sub-directories for any of these folder. As example we create two for Alpha diversity
dir.create("AlphaDiversity/Tables")
dir.create("AlphaDiversity/Figures")

# Store beta diversity results  
dir.create("BetaDiversity")

# Store composition results  
dir.create("Composition")

dir.create("PhyloseqObjects")

Install

This step will check for installed packages and dependencies.

# List of packages for session
.packages <- c("data.table", "plyr", "dplyr", "tidyr", "ggplot2",
               "viridis", "phyloseq", "ggrepel", "Matrix",
               "RColorBrewer",
               "devtools", "picante")

# Install CRAN packages
.inst <- .packages %in% installed.packages()
if(any(!.inst)) {
  install.packages(.packages[!.inst], repos = "http://cran.rstudio.com/")
}

if (!("microbiome"  %in% installed.packages())) {
devtools::install_github("microbiome/microbiome") # Install the package
}

# install.packages("picante",repos="http://R-Forge.R-project.org") 

allneededpackages <- c(.packages, "microbiome")

# Load packages into session
sapply(allneededpackages, require, character.only = TRUE)

#generate a random number 
n <- runif(1, 0, 10^6) # keep this number stored as below instead of XXX when re running the codes.

message("random number set.seed is below")
print(n)

set.seed(n)

seed number(XXX) check this link for more information

library(microbiome)
library(microbiomeutilities)
# install.packages("picante",repos="http://R-Forge.R-project.org") 
# library(picante)
library(picante)
library(data.table)
library(DT)
library(RColorBrewer)
library(phyloseq)
library(tibble)
library(ggpubr)

Initiate

This code chunk reads the input files and creates a phyloseq object.

ps0 <- read_phyloseq(otu.file = otufile, 
                     metadata.file = mapping,
                     taxonomy.file = taxonomy, 
                     type = "biom")

if (!is.na(treefilename)){
  tree <- read.tree(treefilename)

ps0 <- merge_phyloseq(ps0, tree)
} else{
  message("No tree available")
}


saveRDS(ps0, paste0(out_dir, "/PhyloseqObjects/ps_raw.rds"))

message("Raw phyloseq object, confirm the number of samples and variables (as in columns of mapping file)")
message("Below is the content of raw phyloseqobject stored as ps_raw.rds")

print_ps(ps0)

Taxonomic assignments

Below is the percentage of taxonomic assignments at each level.
Note: Only patterns such as [g] or similar is expected. [g] or [g__unclassified] not considered.

DT::datatable(percent_classified(ps0))

Taxonomic summary

An overview of Phylum level abundances in total dataset.

DT::datatable(taxa_summary(ps0, "Phylum"))

QC {.tabset}

print("Below is the summary of the phyloseq object")

summarize_phyloseq(ps0)

Library sizes (density plot)

Check the library sizes for each of the samples within the main variable. Do you think it is okay or there is difference in library sizes and will this affect you downstream statistical analysis?

SeqDepth <- colSums(otu_table(ps0))

sample_data(ps0)$SeqDepth <- SeqDepth

meta.df <- meta(ps0)

qc_plot1 <- plot_read_distribution(ps0, groups= VariableA, plot.type= 'density')

print(qc_plot1)

ggsave(paste0(out_dir,"/QC/ReadDistribution_density.pdf"))

message("QC plots for Read Distribution stored in QC folder as ReadDistribution.pdf")

Library sizes (histogram plot)

message("Investigating library sizes")
SeqDepth <- colSums(otu_table(ps0))

sample_data(ps0)$SeqDepth <- SeqDepth

meta.df <- meta(ps0)

lib.hist <- ggplot(meta.df, aes(x = SeqDepth)) + geom_histogram() + 
  facet_wrap(~meta.df[,VariableA]) +
  xlab("Library size")

print(lib.hist)

ggsave(paste0(out_dir,"/QC/ReadDistribution_density_hist.pdf"))

message("QC plots for library sizes stored in QC folder as ReadDistribution_density_hist.pdf")

Sample size filtering (conditional)

If any sample has less than 2000 reads, it will be removed

if(min(sample_sums(ps0)) < 2000){
  print("There are sample(s) less than 2000 reads, these will be removed")

  # Check sample names before filtering 


  samples_b4_filter <- phyloseq::sample_names(ps0)

  ps0 <- prune_samples(sample_sums(ps0)>=2000, ps0)

  samples_af4_filter <- phyloseq::sample_names(ps0)

  samples_removed <- setdiff(samples_b4_filter, samples_af4_filter)
  print(paste("Following samples were had less than 2000 reads- ", samples_removed))
} else {

  print("No samples below 2000 reads")
  print_ps(ps0)
}

OTU counts distribution (density)

message("Investigating OTU counts distribution")

taxasums = rowSums(otu_table(ps0))
taxatable <- as.data.frame.matrix(tax_table(ps0))


tax_plot1 <- ggplot(taxatable, aes(x = taxasums, color = taxatable[,"Phylum"])) + 
  geom_line(size = 1.5, stat = "density") +
  xlab("OTU Counts") + theme_bw() + scale_x_log10() 
print(tax_plot1)

ggsave(paste0(out_dir,"/QC/Distribution_OTU_Counts_by_phyla.pdf"), height = 6, width = 14)

OTU counts distribution (histogram)

message("Investigating OTU counts distribution")

tax_plot2 <- ggplot(taxatable, aes(x = taxasums, fill = taxatable[,"Phylum"])) + 
   geom_histogram(bins = 30, alpha = 0.5, position = "identity") +
  xlab("OTU Counts") + theme_bw() + scale_x_log10() 
print(tax_plot2)

ggsave(paste0(out_dir,"/QC/Distribution_OTU_Counts_by_phyla_hist.pdf"), height = 6, width = 10)
message("QC plots for library sizes stored in QC folder as Distribution_OTU_Counts.pdf")

Taxa prevalence

Check which of the OTUs are present in low abundance and low prevalence. You might want to remove them depending on the research question.

# for sanity
prev.plot <- plot_taxa_prevalence(ps0, "Phylum")
prev.plot

ggsave(paste0(out_dir,"/QC/OTU_prevalence_phyla.pdf"), height = 8, width = 16)

# for sanity
ps1 <- prune_taxa(taxa_sums(ps0) > 0, ps0)

Variance (raw)

This is variance for all OTU counts without filtering for min number of reads/OTU and prevalence.

Variance.plot.a <- qplot(log10(apply(otu_table(ps1), 1, var)), 
                         xlab = "log10(variance)", 
                         main = "Variance in OTUs") + 
  ggtitle("before filtering") + theme_minimal()

print(Variance.plot.a)

ggsave(paste0(out_dir, "/QC/Variance before filtering.pdf"))

message("QC plots for OTU variance stored in QC folder as Variance before filtering.pdf")

Variance (filtered)

This is variance for all OTU counts after filtering for min number of reads/OTU and prevalence.

if (filterpseq == TRUE) {
message(paste0("Filtering OTUs with less than ", filterCount, " counts"))
message(paste0("in at least ", filterPrev*100, " % of the samples "))

ps2 <- filter_taxa(ps1, function(x) sum(x > filterCount) > (filterPrev * length(x)), TRUE)

message("Saving the transformed phyloseq object as ps_filtered.rds")

saveRDS(ps2, paste0(out_dir,"/PhyloseqObjects/ps_filtered.rds"))

message("Below is the content of filtered phyloseqobject (based on filterCount and filterPrev) stored as ps_filtered.rds")

print_ps(ps2) 

Variance.plot.b <- qplot(log10(apply(otu_table(ps2), 1, var)), 
                         xlab = "log10(variance)", 
                         main = "Variance in OTUs") + 
  ggtitle("after filtering") + 
  theme_minimal()

print(Variance.plot.b)
ggsave(paste0(out_dir,"/QC/Variance After filtering.pdf"))

} else 
  {

    message("filterpseq was false. Did not filter and hence will not save the filtered phyloseq")

    ps2 <- ps1

}

Coefficient of variation (raw)

This is coefficient of variation for all OTU counts without filtering for min number of reads/OTU and prevalence.

cv_plot <- plot_taxa_cv(ps1, "hist")
print(cv_plot)

ggsave(paste0(out_dir, "/QC/Coefficient of variation before filtering.pdf"))

message("QC plots for OTU variance stored in QC folder as Variance before filtering.pdf")

Coefficient of variation (filtered)

This is coefficient of variation for all OTU counts after filtering for min number of reads/OTU and prevalence.

if (filterpseq == TRUE) {
message(paste0("Filtering OTUs with less than ", filterCount, " counts"))
message(paste0("in at least ", filterPrev*100, " % of the samples "))

ps2 <- filter_taxa(ps1, function(x) sum(x > filterCount) > (filterPrev * length(x)), TRUE)

message("Using the filtered phyloseq object as ps_filtered.rds")

message("Below is the content of filtered phyloseqobject (based on filterCount and filterPrev) stored as ps_filtered.rds")

print_ps(ps2) 

cv_plot <- plot_taxa_cv(ps2, "hist")
print(cv_plot)
ggsave(paste0(out_dir,"/QC/Coefficient of variation After filtering.pdf"))

} else 
  {

    message("filterpseq was false. Did not filter and hence will not save the filtered phyloseq")

    ps2 <- ps1

}

Measuring the kurtosis

Kurtosis is essentially a measure of how much weight is at the tails of the distribution relative to the weight around the location.
If you have large differences in your library sizes then you may have to normalise your data.

Kurtosis

message("Using the raw phyloseq to check for kurtosis in library size")

df <- data.table(NumberReads = sample_sums(ps0), SampleID = sample_names(ps0))
require(moments)
n <- kurtosis(df$NumberReads) 

if (n > 3) {
    message("Your library size is heavily tailed, considering normalising them for further analysis")
  }  else {

    message("The variation in library sizes is below kurtosis value of 3 may indicate no need for rarefying")

}

Alpha diversity {.tabset}

Alpha diversity measures are standard calculations in microbial ecology. The differences in richness and eveness between groups may have importance to understanding the ecology. There are numerous measures we use the defaults from microbiome R package and also the phylogenetic diversity from picanteR package.

The caculations can be done on rarefied or non-rarefied data which can be specified by the samsize option in "Set project attributes" option above.

Non-phylogenetic diversity measures

Below you can find

For more on this check Microbiome:Diversities

if (!is.na(samsize)) {

  ps3 <- rarefy_even_depth(ps2, sample.size = samsize)

  saveRDS(ps3, paste0(out_dir, "/phyloseqObjects/ps_rarefyied.rds"))
} else{
  ps3 <- ps2
}

metadf <- meta(ps3)
metadf$sam_rep_nw <- rownames(metadf)

adiv.meta <- estimate_richness(ps3)
colnames(adiv.meta)

adiv.meta$sam_rep_nw <- rownames(adiv.meta)

adiv.nw <- reshape2::melt(adiv.meta)
colnames(adiv.nw) <- c("sam_rep_nw","Diversity","div.val")
meta_df_nw <- reshape2::melt(metadf)
meta_adiv <- merge.data.frame(meta_df_nw, adiv.nw, by = "sam_rep_nw")
colnames(meta_adiv)

p <- ggqqplot(meta_adiv, "div.val", 
              facet.by = c("Diversity", VariableA), 
              color = VariableA)
p <- facet(p , facet.by = c("Diversity", VariableA), scales = "free")
print(p)

#Create 2x2 plot environment 
ggsave(paste0(out_dir,"/AlphaDiversity/Non-phylogenetic_alpha_diversity_qqnorm.pdf"), height = 8, width= 12)


#shapiro.test
shaOb <- shapiro.test(adiv.meta$Observed)

shaChao1 <- shapiro.test(adiv.meta$Chao1)
shaACE <- shapiro.test(adiv.meta$ACE)
shaShannon <- shapiro.test(adiv.meta$Shannon)
shaSimpson <- shapiro.test(adiv.meta$Simpson)
shaInvSimpson <- shapiro.test(adiv.meta$InvSimpson)
shaFisher <- shapiro.test(adiv.meta$Fisher)

Diversity_Metric <- c("Observed", "Chao1","ACE","Shannon","Simpson","InvSimpson","Fisher")
shapiro.test.statistic <- c(shaOb$statistic, shaChao1$statistic, shaACE$statistic, shaShannon$statistic, shaSimpson$statistic, shaInvSimpson$statistic, shaFisher$statistic)
shapiro.test.p.value <- c(shaOb$p.value, shaChao1$p.value, shaACE$p.value, shaShannon$p.value, shaSimpson$p.value, shaInvSimpson$p.value, shaFisher$p.value)


divtab <- cbind(Diversity_Metric, round(shapiro.test.statistic, 3), shapiro.test.p.value)
rownames(divtab) <- paste(seq(1:7))
colnames(divtab) <- c("Diversity_Metric","shapiro.test.statistic","shapiro.test.p.value")

DT::datatable(divtab)

alpha_div <- plot_richness(ps3, x = VariableA,
                           color = VariableB, 
                           measures = c("Observed", 
                                        "Chao1", 
                                        "Shannon", 
                                        "InvSimpson")) + geom_boxplot()

alpha_div <- alpha_div + theme_bw() + geom_point(size = 2) + ggtitle("Non phylogenetic diversity") + scale_color_brewer(palette = col.palette) + rotate_x_text()

print(alpha_div)
ggsave(paste0(out_dir,"/AlphaDiversity/Non-phylogenetic_alpha_diversity.pdf"), 
              height = 6, width = 18)

if (!is.na(samsize)){
  message("Non-phylogenetic_alpha_diversity on RAREFIED data stored in AlphaDiversity folder")
  message("Non-phylogenetic_alpha_diversity.pdf")
} else{
  message("Non-phylogenetic_alpha_diversity on NON-RAREFIED data stored  in AlphaDiversity folder")
  message("Non-phylogenetic_alpha_diversity.pdf")
}

Phylogenetic diversity measures

For more on this check Picante.

if (!is.na(treefilename)){

   message("If sam.size was provided then rarefyied phyloseq object will be used to calculate PD")
  print(ps3)
  otu_table_ps3 <- as.data.frame(ps3@otu_table)
  metadata_table_ps3  <- meta(ps3)

 message("include.root in pd is set to FALSE by default")

 df.pd <- pd(t(otu_table_ps3), tree,include.root=F) # t(ou_table) transposes the table for use in picante and the tre file comes from the  first code chunck we used to read tree file (see making a phyloseq object section).


 datatable(df.pd)
# now we need to plot PD
# check above how to get the metadata file from phyloseq object.
# We will add the results of PD to this file and then plot.
 select.meta <- metadata_table_ps3[,c(VariableA,VariableB)] #, "Phyogenetic_diversity"]
 select.meta$Phyogenetic_diversity <- df.pd$PD 
 colnames(select.meta) <- c("VariableA", "VariableB", "Phyogenetic_diversity")

 shapiro.test(select.meta$Phyogenetic_diversity)

 qqnorm(select.meta$Phyogenetic_diversity)

 plot.pd <- ggplot(select.meta, aes(VariableA, Phyogenetic_diversity)) + 
   geom_boxplot(aes(fill = VariableB)) + geom_point(size = 2) +  
   theme(axis.text.x = element_text(size=14, angle = 90)) + 
   theme_bw() + scale_fill_brewer(palette = col.palette)

 print(plot.pd)

 ggsave(paste0(out_dir, "/AlphaDiversity/Phylogenetic_diversityon_nonRafrefied_data.pdf"), 
         plot = plot.pd, 
         height = 6, width = 18)

} else{

  message("No tree supplied, PD cannot be calculated")

}

Micrbial community composition {.tabset}

Having a look at the phyloegnetic composition of you data is useful for many reasons. For this purpose we have two Phylum and Family level plots.

Phylum (Barplot)

ps3.com <- ps1 # create a new pseq object
# We need to set Palette
taxic <- as.data.frame(ps3.com@tax_table)  # this will help in setting large color options

colourCount = length(unique(taxic$Phylum))  #define number of variable colors based on number of Family (change the level accordingly to phylum/class/order)
getPalette = colorRampPalette(brewer.pal(12, col.palette))  # change the palette as well as the number of colors will change according to palette.

# now edit the unclassified taxa
tax_table(ps3.com)[tax_table(ps3.com)[, "Phylum"] == "f__", "Phylum"] <- "f__Unclassified Phylum"
# We will also remove the 'f__' patterns for cleaner labels
tax_table(ps3.com)[, colnames(tax_table(ps3.com))] <- gsub(tax_table(ps3.com)[, 
    colnames(tax_table(ps3.com))], pattern = "[a-z]__", replacement = "")

otu.df <- as.data.frame(otu_table(ps3.com))  # make a dataframe for OTU information.
# head(otu.df) # check the rows and columns

taxic$OTU <- row.names.data.frame(otu.df)  # Add the OTU ids from OTU table into the taxa table at the end.
colnames(taxic)  # You can see that we now have extra taxonomy levels.

taxmat <- as.matrix(taxic)  # convert it into a matrix.
new.tax <- tax_table(taxmat)  # convert into phyloseq compaitble file.
tax_table(ps3.com) <- new.tax  # incroporate into phyloseq Object


# it would be nice to have the Taxonomic names in italics.
# for that we set this
guide_italics <- guides(fill = guide_legend(label.theme = element_text(size = 15, 
    face = "italic", colour = "Black", angle = 0)))


## Now we need to plot at family level, We can do it as follows:

# first remove the phy_tree

ps3.com@phy_tree <- NULL

lev0 = "Phylum"
tax_table(ps3.com)[,lev0][is.na(tax_table(ps3.com)[,lev0])] <- paste0(tolower(substring(lev0, 1, 1)), "__")


ps3.com.phy <- aggregate_taxa(ps3.com, "Phylum")

ps3.com.phy.rel <- microbiome::transform(ps3.com.phy, "compositional")

plot.composition.relAbun.phy <- plot_composition(ps3.com.phy.rel) + theme(legend.position = "bottom") + 
    scale_fill_manual(values = getPalette(colourCount)) + theme_bw() + 
    theme(axis.text.x = element_text(angle = 90)) + 
  ggtitle("Relative abundance Phylum level") + guide_italics 

plot.composition.relAbun.phy

if (nrow(metadf) > 30) {
  ggsave(paste0(out_dir, "/Others/compositionbarplot_Phylum.pdf"), plot = plot.composition.relAbun.phy, height = 8, width = 28)

} else {
    ggsave(paste0(out_dir, "/Others/compositionbarplot_Phylum.pdf"), plot = plot.composition.relAbun.phy, 
      height = 8, width = 18)
}

Phylum (Boxplot)

Top 5 Phyla are shown below:

pn0 <- plot_taxa_boxplot(ps3.com, 
                        taxonomic.level = "Phylum", 
                        top.otu = 6, VariableA, 
                        title = "Relative abundance Phylum level", color = "Paired")
pn0
ggsave(paste0(out_dir,"/Others/compositionboxplot_Phylum.pdf"), height = 6, width = 12)

Family (Barplot)

colourCount = length(unique(taxic$Family))  #define number of variable colors based on number of Family (change the level accordingly to phylum/class/order)
getPalette = colorRampPalette(brewer.pal(12, col.palette))  # change the palette as well as the number of colors will change according to palette.
lev = "Family"
tax_table(ps3.com)[,lev][is.na(tax_table(ps3.com)[,lev])] <- paste0(tolower(substring(lev, 1, 1)), "__")

ps3.com.fam <- aggregate_taxa(ps3.com, "Family")

ps3.com.fam.rel <- microbiome::transform(ps3.com.fam, "compositional")

plot.composition.relAbun.fam <- plot_composition(ps3.com.fam.rel) + theme(legend.position = "bottom") + 
    scale_fill_manual(values = getPalette(colourCount)) + theme_bw() + 
    theme(axis.text.x = element_text(angle = 90)) + 
  ggtitle("Relative abundance Family level") + guide_italics 

plot.composition.relAbun.fam

if (nrow(metadf) > 30) {
  ggsave(paste0(out_dir,"/Others/compositionbarplot_Family.pdf"), plot = plot.composition.relAbun.fam, height = 8, width = 28)

} else {
    ggsave(paste0(out_dir,"/Others/compositionbarplot_Family.pdf"), plot = plot.composition.relAbun.fam, 
      height = 8, width = 18)
}

Family (Boxplot)

Top 10 Families are shown below:

pn1 <- plot_taxa_boxplot(ps3.com, 
                        taxonomic.level = "Family", 
                        top.otu = 10, VariableA, 
                        title = "Relative abundance Family level", color = "Paired")

pn1
ggsave(paste0(out_dir,"/Others/compositionboxplot_Family.pdf"), height = 6, width = 16)

Beta Diversity (Ordinations) {.tabset}

Bray-Curtis distance PCoA

The counts are compositionally tranformed and then used for ordinations.

ps3.rel <- microbiome::transform(ps3, "compositional")

bc.pcoa <- phyloseq::ordinate(ps3.rel, method = "PCoA", distance = "bray")

bc.pcoa.plot <- plot_ordination(ps3.rel, bc.pcoa, 
                                type = "split", axes = 1:2,
                                color = VariableA, shape = VariableB, 
                                label = NULL, 
                                title = "Bray-Curtis distance PCoA",
                                justDF = FALSE)
bc.pcoa.plot <- bc.pcoa.plot + theme_bw() + geom_point(size = 2)
print(bc.pcoa.plot)

ggsave(paste0(out_dir,"/BetaDiversity/Bray-Curtis distance PCoA.pdf"), plot = bc.pcoa.plot, 
      height = 6, width = 10)


# Calculate bray curtis distance matrix
ps3_bray <- phyloseq::distance(ps3.rel, method = "bray")

# use meta data from phylogenetic div code chunk.
metadata_table_ps3  <- meta(ps3.rel)
select.meta2 <- metadata_table_ps3[,c(VariableA,VariableB)] #, "Phyogenetic_diversity"]

colnames(select.meta2) <- c("VariableA", "VariableB")

# Adonis test
adonis(ps3_bray ~ VariableA, data = select.meta2)


# Homogeneity of dispersion test
beta.bray <- betadisper(ps3_bray, select.meta2$VariableA)
permutest(beta.bray)

Weighted Unifrac distance PCoA

The counts are converted to relative abundance and then used for ordinations.

if (!is.na(treefilename)){

wunifrac.pcoa <- ordinate(ps3.rel, method = "PCoA", distance = "wunifrac")

wunifrac.pcoa.plot <- plot_ordination(ps3.rel, wunifrac.pcoa, 
                                type = "split", axes = 1:2,
                                color = VariableA, shape = VariableB, 
                                label = NULL, 
                                title = "Weighted Unifrac distance PCoA",
                                justDF = FALSE)
wunifrac.pcoa.plot <- wunifrac.pcoa.plot + theme_bw() + geom_point(size = 2)
print(wunifrac.pcoa.plot)

ggsave(paste0(out_dir,"/BetaDiversity/Weighted Unifrac distance PCoA.pdf"), plot = wunifrac.pcoa.plot, 
      height = 6, width = 10)

# Calculate bray curtis distance matrix
ps3_wunifrac <- phyloseq::distance(ps3.rel, method = "wunifrac")

# use meta data from phylogenetic div code chunk.

# Adonis test
adonis(ps3_wunifrac ~ VariableA, data = select.meta2)


# Homogeneity of dispersion test
beta.wunifrac <- betadisper(ps3_wunifrac, select.meta2$VariableA)
permutest(beta.wunifrac)
} else {
  message("No tree supplied, cannot calculate unifrac distances")
}

Unweighted Unifrac distance PCoA

The counts are converted to relative abundnces and then used for ordinations.

if (!is.na(treefilename)){

unifrac.pcoa <- ordinate(ps3.rel, method = "PCoA", distance = "unifrac")

unifrac.pcoa.plot <- plot_ordination(ps3.rel, unifrac.pcoa, 
                                type = "split", axes = 1:2,
                                color = VariableA, shape = VariableB, 
                                label = NULL, 
                                title = "Unweighted Unifrac distance PCoA",
                                justDF = FALSE)
unifrac.pcoa.plot <- unifrac.pcoa.plot + theme_bw() + geom_point(size = 2)
print(unifrac.pcoa.plot)

ggsave(paste0(out_dir,"/BetaDiversity/Unweighted Unifrac distance PCoA.pdf"), plot = unifrac.pcoa.plot, 
      height = 6, width = 10)

message("Unweighted Unifrac distance gives negative values and standard anova and anoism cannot be used")

} else {
  message("No tree supplied, cannot calculate unifrac distances")
}

Heatmap

if (heatmap == TRUE) {

heat.sample <- plot_taxa_heatmap(ps3, subset.top = 50,
                                 VariableA = VariableA,
                                 heatcolors =brewer.pal(9, "Blues"),
                                 transformation = "compositional",
                                 file = "./Others/Heatmap rel abun top 50 OTUs.tiff",
                                 height = 9, width = 10)
print(paste0("heatmap saved in ", out_dir))

# Duplicate for printing in the HTML file  
heat.sample.dup <- plot_taxa_heatmap(ps3, subset.top = 50,
                                 VariableA = VariableA,
                                 heatcolors =brewer.pal(9, "Blues"),
                                 transformation = "compositional")


}

R session information and parameters

The versions of the R software and Bioconductor packages used for this analysis are listed below. It is important to save them if one wants to re-perform the analysis in the same conditions.

sessionInfo()

microsud/microbiomeutilities documentation built on Nov. 29, 2022, 12:18 a.m.