2023_06_23_core_microbiome.Rmd

---
title: "Untitled"
author: "kim soyeon"
date: "2023-06-22"
output: html_document
---

참고1 : https://microbiome.github.io/tutorials/Core.html
참고2 : https://mibwurrepo.github.io/Microbial-bioinformatics-introductory-course-Material-2018/core-microbiota.html
```{r}
library(phyloseq)
library(ggplot2)
library(microbiome)
library(dplyr)
library(RColorBrewer)

ps <- readRDS("./ps.rds") 
# phyloseq-class experiment-level object
# otu_table()   OTU Table:         [ 770 taxa and 34 samples ]
# sample_data() Sample Data:       [ 34 samples by 8 sample variables ]
# tax_table()   Taxonomy Table:    [ 770 taxa by 7 taxonomic ranks ]
# phy_tree()    Phylogenetic Tree: [ 770 tips and 768 internal nodes ]

ps.rel <- transform_sample_counts(ps, function(x) x/sum(x) )

```

```{r}
# Relative population frequencies; at 1% compositional abundance threshold:
Pre <- prevalence(ps.rel, detection = 1/100, sort = TRUE)
head(Pre)
  # Absolute population frequencies (sample count):
head(prevalence(ps.rel, detection = 1/100, sort = TRUE, count = TRUE))
```

```{r}
core.taxa.standard <- core_members(ps.rel, detection = 1/100, prevalence = 50/100)


pseq.core <- core(ps.rel, detection = 0.1/100, prevalence = 50/100)
core.members <- core_members(ps.rel, detection = 0, prevalence = 50/100)
```


# Core microbiota analysis
```{r}
p.core <- plot_core(ps.rel, 
                    plot.type = "heatmap", 
                    colours = rev(brewer.pal(5, "Spectral")),
                    # prevalences = prevalences, 
                    # detections = detections, 
                    min.prevalence = .5) 
p.core

```