Metagenome Data Analysis using Phyloseq
Learning Goals
At the end of this exercise, you will be able to: 1. Explore metagenomic data in R. 2. Modify data for using phyloseq. 3. Calculate diversity metrics and make resulting plots.
Resources
Install Phyloseq
Be sure to re-comment out after installing
#install.packages(phyloseq)Load Libraries
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.0.4
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(janitor)##
## Attaching package: 'janitor'
##
## The following objects are masked from 'package:stats':
##
## chisq.test, fisher.testlibrary(ggplot2)
library(phyloseq)Data
For this tutorial, we will use: KOSTIC_INFANTS_DIABETES_2015. The data are from: Kostic AD, et al (2015) The Dynamics of the Human Infant Gut Microbiome in Development and in Progression towards Type 1 Diabetes. These data are a modified from the borenstein-lab’s repository microbiome-metabolome-curated-data
OTU Table
otu_mat <- read.csv("data/OTU_Table_KOSTIC_INFANTS_DIABETES.csv")Taxonomy Table
tax_mat <- read.csv("data/Tax_Table_KOSTIC_INFANTS_DIABETES.csv") Sample Data
sample_df <- read.csv("data/Sample_Data_KOSTIC_INFANTS_DIABETES.csv") Explore
Use the function of your choice to provide to explore each dataframe.
Manipulate Data for Phyloseq
otu_mat <- otu_mat %>%
tibble::column_to_rownames("X")
tax_mat <- tax_mat %>%
tibble::column_to_rownames("X")sample_df <- sample_df %>%
tibble::column_to_rownames("Sample") The OTU table and taxonomy table of a phyloseq object must be a matrix and not a dataframe
otu_mat <- as.matrix(otu_mat)
tax_mat <- as.matrix(tax_mat)Create a Phyloseq Object
otu <- otu_table(otu_mat, taxa_are_rows = TRUE)
tax <- tax_table(tax_mat)
samples <- sample_data(sample_df)
ps <- phyloseq(otu, tax, samples)
ps## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 584 taxa and 103 samples ]
## sample_data() Sample Data: [ 103 samples by 67 sample variables ]
## tax_table() Taxonomy Table: [ 584 taxa by 6 taxonomic ranks ]Normalize the Data
Use total sum scaling to normalize the data
total <- median(sample_sums(ps))
standf <- function(x, t=total) round(t * (x / sum(x)))
ps <- transform_sample_counts(ps, standf)Alpha Diversity
What if we want the alpha diversity of our data. Alpha diversity measures numbers of each genera and the evenness or distribution of a genera.
plot_richness(ps, x = "Study.Group", measures = c("Shannon"), color = "Study.Group")
We can also do multiple diversity indexes and even add layers to this plot similar to ggplot
plot_richness(ps, x="Study.Group", measures=c("Shannon", "Chao1"), color = "Study.Group", title = "Alpha Diversity Infants Predisposed to T1D")+
labs(x = "Study Group")
Practice
Make your own plot, but also include Observed and Simpson indexes.
Costumize as desired including the colors using
scale_colour_manual. Store in an object called
alpha_div.
alpha_div <- plot_richness(ps, x="Study.Group", measures=c("Shannon", "Chao1", "Observed", "Simpson"), color = "Study.Group", title = "Alpha Diversity Infants Predisposed to T1D") + scale_colour_manual(values = c("darkgreen", "orchid4"), labels =c("diabetic", "none diabetic")) +
labs(x = "Study Group") +
geom_boxplot(alpha = 0.2)+
scale_x_discrete(labels = c("case" = "diabetic", "control" = "none diabetic"))
alpha_div
remove a layer
alpha_div$layers <- alpha_div$layers[-1]
alpha_div
Beta Diversity
Calculate Bray-Curtis distance matrix
# Calculate Bray-Curtis distance matrix
dis_mat <- distance(ps, method = "bray")Do PCoA using the Bray-Curtis distance matrix
# Perform PCoA using the Bray-Curtis distance matrix
pcoa_data <- ordinate(ps, method = "PCoA", distance = dis_mat)Plot a PCoA
# Plot the PCoA results
plot_ordination(ps, pcoa_data, color = "Study.Group") +
geom_point(size = 4) +
ggtitle("PCoA: Bray-Curtis Dissimilarity") 
Practice
Make a NMDS plot and customize
# Perform NMDS using the Bray-Curtis distance matrix
pcoa_data <- ordinate(ps, method = "NMDS", distance = dis_mat)## Run 0 stress 0.2217738
## Run 1 stress 0.2210486
## ... New best solution
## ... Procrustes: rmse 0.0607475 max resid 0.2211814
## Run 2 stress 0.2162984
## ... New best solution
## ... Procrustes: rmse 0.05537206 max resid 0.216754
## Run 3 stress 0.2419829
## Run 4 stress 0.2162302
## ... New best solution
## ... Procrustes: rmse 0.01739177 max resid 0.1488482
## Run 5 stress 0.2213459
## Run 6 stress 0.2214612
## Run 7 stress 0.221462
## Run 8 stress 0.2212507
## Run 9 stress 0.2403346
## Run 10 stress 0.2168161
## Run 11 stress 0.2167173
## ... Procrustes: rmse 0.02473916 max resid 0.2029574
## Run 12 stress 0.2166986
## ... Procrustes: rmse 0.02439824 max resid 0.2013544
## Run 13 stress 0.2244308
## Run 14 stress 0.2218863
## Run 15 stress 0.2212613
## Run 16 stress 0.2269254
## Run 17 stress 0.2429551
## Run 18 stress 0.2201566
## Run 19 stress 0.216237
## ... Procrustes: rmse 0.01283954 max resid 0.1018117
## Run 20 stress 0.2290763
## *** Best solution was not repeated -- monoMDS stopping criteria:
## 3: no. of iterations >= maxit
## 17: stress ratio > sratmax# Plot the NMDS results
plot_ordination(ps, pcoa_data, color = "Study.Group") +
geom_point(size = 4) +
ggtitle("NMDS: Bray-Curtis Dissimilarity")
# Plot the NMDS results
plot_ordination(ps, pcoa_data, color = "Study.Group", shape = "AAB_positive") +
geom_point(size = 4) +
scale_colour_manual(values = c("darkgreen", "orchid4"),
labels =c("diabetic", "nonn-diabetic")) +
labs(color = "study group", shape = "ABA", title = "NMDS: Bray-Curtis Dissimilarity" ) +
scale_shape_manual(values= c(16, 17),
labels =c("FALSE" = "negative", "TRUE" = "positive"))
Taxonomy
Make a taxonomy plot
plot_bar(ps, fill = "phylum")
plot_bar(ps, x = "Subject", y = "Abundance", fill = "phylum") +
geom_bar(aes(color=phylum, fill=phylum), stat="identity", position="stack")
Subset the data
Lets subset the data for intants at or over 600 days old
sample_rn <- rownames(age_sd)Match the rownames to the otu table
age_otu <- otu_mat %>%
as.data.frame() %>%
select(all_of(sample_rn))
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## genera584 45 0 23 0 0 2 10Create a new phyloseq object
ps_age<- phyloseq(
otu_table(as.matrix(age_otu), taxa_are_rows = T),
tax_table(as.matrix(tax_mat)),
sample_data(age_sd))Make a new taxonomy plot
plot_bar(ps_age, fill = "phylum")
plot_bar(ps_age, x = "Subject", y = "Abundance", fill = "phylum") +
geom_bar(aes(color=phylum, fill=phylum), stat="identity", position="stack") +
facet_wrap(Study.Group~.)
Relative Abundance
Lets take the Relative abundance of our genera
ps_phylum <- ps_age %>%
tax_glom(taxrank = "phylum") %>% # agglomerate at phylum level
transform_sample_counts(function(x) {x/sum(x)} ) %>% # Transform to rel. abundance
psmelt() Make new plot based on relative abundance data
ggplot(ps_phylum, aes(x = Subject, y = Abundance, fill = phylum)) +
geom_bar(stat = "identity") +
facet_wrap(Study.Group~.) +
coord_flip()+
ylab("Relative Abundance") +
ggtitle("Phylum Composition Toddlers Predisposed for T1D") +
scale_fill_discrete(labels = c("Firmicutes_C" = "Firmicutes C", "Firmicutes_A" = "Firmicutes A", "Firmicutes_B" = "Firmicutes B"))
Practice
Try creating a graph for class instead of phylum
ps_class <- ps_age %>%
tax_glom(taxrank = "class") %>% # agglomerate at phylum level
transform_sample_counts(function(x) {x/sum(x)} ) %>% # Transform to rel. abundance
psmelt() ggplot(ps_class, aes(x = Subject, y = Abundance, fill = class)) +
geom_bar(stat = "identity") +
facet_wrap(Study.Group~.) +
coord_flip()+
ylab("Relative Abundance") +
ggtitle("Phylum Composition Toddlers Predisposed for T1D") 
Bonus
Try replotting your alpha and beta diversity plots with the phyloseq object that accounts for age >600.
Alpha Diversity
plot_richness(ps_age, x="Study.Group", measures=c("Shannon", "Chao1", "Observed", "Simpson"), color = "Study.Group", title = "Alpha Diversity Infants Predisposed to T1D") + scale_colour_manual(values = c("darkgreen", "orchid4"), labels =c("diabetic", "none diabetic")) +
labs(x = "Study Group") +
geom_boxplot(alpha = 0.2)+
scale_x_discrete(labels = c("case" = "diabetic", "control" = "none diabetic"))## Warning in estimate_richness(physeq, split = TRUE, measures = measures): The data you have provided does not have
## any singletons. This is highly suspicious. Results of richness
## estimates (for example) are probably unreliable, or wrong, if you have already
## trimmed low-abundance taxa from the data.
##
## We recommended that you find the un-trimmed data and retry.
# Calculate Bray-Curtis distance matrix
dis_mat <- distance(ps_age, method = "bray")
# Perform PCoA using the Bray-Curtis distance matrix
pcoa_data <- ordinate(ps_age, method = "PCoA", distance = dis_mat)
# Plot the PCoA results
plot_ordination(ps_age, pcoa_data, color = "Study.Group") +
geom_point(size = 4) +
ggtitle("PCoA: Bray-Curtis Dissimilarity")
Wrap-up
Be sure to save, knit and push your work.
Time to work on Group Projects!