Chapter 9 Core microbiota

These functions in this chapter are from (Leo Lahti 2018) package. The core microbiota are passed the parameters’ cutoff (eg. prevalence, abundance).

Core microbiota, playing an important role on the interaction between host and environment is the dominant taxa in the community. Here, we identify them by using prevalence across samples and the limit of detection of abundance.

Outline of this Chapter:

9.1 Loading Packages

library(XMAS2)
library(dplyr)
library(tibble)
library(phyloseq)
library(ggplot2)
library(ggpubr)

9.2 Importing Data

Removing BRS
Rarefying counts
Extracting genus level phyloseq

data("dada2_ps")
dada2_ps_remove_BRS <- get_GroupPhyloseq(
                         ps = dada2_ps,
                         group = "Group",
                         group_names = "QC",
                         discard = TRUE)

# Rarefying counts
dada2_ps_rarefy <- norm_rarefy(object = dada2_ps_remove_BRS, 
                               size = 51181)

# Genus level
dada2_ps_rarefy_genus <- summarize_taxa(ps = dada2_ps_rarefy, 
                                        taxa_level = "Genus")

MGS dataset

data("metaphlan2_ps")
metaphlan2_ps_remove_BRS <- get_GroupPhyloseq(
                               ps = metaphlan2_ps,
                               group = "Group",
                               group_names = "QC",
                               discard = TRUE)
metaphlan2_ps_remove_BRS

## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 326 taxa and 22 samples ]
## sample_data() Sample Data:       [ 22 samples by 2 sample variables ]
## tax_table()   Taxonomy Table:    [ 326 taxa by 7 taxonomic ranks ]

9.3 Obtaining core microbiota

Normalization: See the Chapter 6 Pre-processing

dada2_ps_rarefy_genus_rb <- XMAS2::normalize(object = dada2_ps_rarefy_genus, 
                                             method = "TSS")
dada2_ps_rarefy_genus_rb

## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 198 taxa and 23 samples ]
## sample_data() Sample Data:       [ 23 samples by 1 sample variables ]
## tax_table()   Taxonomy Table:    [ 198 taxa by 6 taxonomic ranks ]

Check for the core ASVs

Use core_members to obtain the core taxa. detection for abundance and prevalence for occurrence.

core_taxa_name <- core_members(dada2_ps_rarefy_genus_rb, 
                               detection = 0.01, 
                               prevalence = 0.8)
print(core_taxa_name)

## [1] "g__Bifidobacterium"              "g__Blautia"                      "g__Lachnospiraceae_unclassified"

Result:

Only three genera (g__Bifidobacterium, g__Blautia and g__Lachnospiraceae_unclassified) passed the threshold of detection and prevalence which we choose.

9.4 Showing core abundance and diversity

Total core abundance in each sample (sum of abundances of the core members):

core_abundance <- sample_sums(phyloseq_core_taxa(dada2_ps_rarefy_genus_rb, 
                                                 detection = 0.01, 
                                                 prevalence = 0.8))

DT::datatable(data.frame(core_abundance))

9.5 Visualizing core taxa

We display the taxa based on the prevalence and detection via two ways (heatmap or linechart plot).

9.5.1 heatmap

Core with composition

prevalences <- seq(0.05, 1, 0.05)
detections <- 10^seq(log10(1e-3), log10(.2), length = 10)

pl_core <- plot_core_taxa(dada2_ps_rarefy_genus_rb, 
                    plot.type = "heatmap", 
                    colours = gray(seq(0, 1, length=5)),
                    prevalences = prevalences, 
                    detections = detections, 
                    min.prevalence = 0.5)+
    xlab("Detection Threshold (Relative Abundance (%))")

pl_core

Figure 9.1: Core with composition in Heatmap

The degree of color indicates the size of abundance and prevalence.

other colors: viridis

library(viridis)
print(pl_core + scale_fill_viridis())

Figure 9.2: Core with composition in Heatmap (viridis)

change color

library(RColorBrewer)

prevalences <- seq(0.05, 1, 0.05)
detections <- 10^seq(log10(1e-3), log10(.2), length = 10)

pl_core <- plot_core_taxa(dada2_ps_rarefy_genus_rb, 
                    plot.type = "heatmap", 
                    colours = rev(brewer.pal(5, "Spectral")),
                    prevalences = prevalences, 
                    detections = detections, 
                    min.prevalence = 0.5)+
    xlab("Detection Threshold (Relative Abundance (%))") +
    theme(axis.text.y = element_text(face="italic"))

pl_core

Figure 9.3: Core with composition in Heatmap (RColorBrewer)

9.5.2 linechart

This plot show the relationship between Detection and Prevalence in a linear model.

prevalences <- seq(0.05, 1, 0.05)
detections <- 10^seq(log10(1e-3), log10(.2), length = 10)

pl_core <- plot_core_taxa(dada2_ps_rarefy_genus_rb, 
                    plot.type = "lineplot", 
                    prevalences = prevalences, 
                    detections = detections, 
                    min.prevalence = 0.5)+
    xlab("Detection Threshold (Relative Abundance (%))") +
    theme(axis.text.y = element_text(face="italic"))

pl_core

Figure 9.4: Core with composition in linechart

When increasing the Detection, the core size turns to low level.

9.6 Systematic Information

devtools::session_info()

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##  [2] /Library/Frameworks/R.framework/Versions/4.1/Resources/library
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References

Leo Lahti, Sudarshan Shetty. 2018. microbiome: Tools for Microbiome Analysis in r. https://github.com/microbiome/microbiome.