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()## ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## setting value
## version R version 4.1.2 (2021-11-01)
## os macOS Monterey 12.2.1
## system x86_64, darwin17.0
## ui RStudio
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz Asia/Shanghai
## date 2022-10-08
## rstudio 2022.07.1+554 Spotted Wakerobin (desktop)
## pandoc 2.18 @ /Applications/RStudio.app/Contents/MacOS/quarto/bin/tools/ (via rmarkdown)
##
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## XVector 0.34.0 2021-10-26 [1] Bioconductor
## yaml 2.3.5 2022-02-21 [1] CRAN (R 4.1.2)
## zCompositions 1.4.0 2022-01-13 [1] CRAN (R 4.1.2)
## zlibbioc 1.40.0 2021-10-26 [1] Bioconductor
## zoo 1.8-9 2021-03-09 [1] CRAN (R 4.1.0)
##
## [1] /Library/Frameworks/R.framework/Versions/4.1/Resources/library
##
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────References
Leo Lahti, Sudarshan Shetty. 2018. microbiome: Tools for Microbiome Analysis in r. https://github.com/microbiome/microbiome.