PICRUSt2怎么用R实现
时间: 2024-05-12 16:21:02 浏览: 11
PICRUSt2是一个用于预测微生物基因组功能的工具,它可以通过基因家族注释和基于参考基因组的比对来对16S rRNA或宏基因组数据进行功能预测。在R中使用PICRUSt2需要先安装相关的R包,然后按照以下步骤操作:
1. 安装依赖包和PICRUSt2
```r
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install(c("phyloseq", "dplyr", "stringr", "tidyr", "magrittr"))
```
```r
if (!requireNamespace("devtools", quietly = TRUE))
install.packages("devtools")
devtools::install_github("picrust/picrust2", build_vignettes = TRUE)
```
2. 加载依赖包和PICRUSt2
```r
library(phyloseq)
library(dplyr)
library(stringr)
library(tidyr)
library(magrittr)
library(picrust2)
```
3. 读取OTU表和物种注释信息
```r
otu_file <- "otu_table.biom"
otu_table <- import_biom(otu_file)
tax_file <- "taxonomy.tsv"
tax_table <- read.delim(tax_file, header = FALSE, stringsAsFactors = FALSE)
colnames(tax_table) <- c("OTU_ID", "taxonomy")
```
4. 处理物种注释信息
```r
tax_table %<>%
separate(taxonomy, into = c("kingdom", "phylum", "class", "order", "family", "genus", "species"), sep = ";")
tax_table$OTU_ID <- str_remove(tax_table$OTU_ID, ".*OTU_")
```
5. 将OTU表和物种注释信息合并成phyloseq对象
```r
tax_table_phylo <- otu_table_to_phyloseq(
otu_table = otu_table,
tax_table = tax_table,
refseq_col = "OTU_ID"
)
```
6. 运行PICRUSt2
```r
picrust2_pipeline(tax_table_phylo, outdir = "picrust2_output")
```
在运行过程中,PICRUSt2会下载和安装必要的参考数据库,并生成预测的功能注释文件。最后,可以使用以下命令读取并处理预测的功能注释文件:
```r
ko_file <- "picrust2_output/metagenome_predictions/ko_metagenome_contributions.tsv.gz"
ko_table <- read.delim(ko_file, header = TRUE, stringsAsFactors = FALSE, check.names = FALSE)
colnames(ko_table)[1] <- "OTU_ID"
ko_table$OTU_ID <- str_remove(ko_table$OTU_ID, ".*OTU_")
ko_table %<>%
pivot_longer(cols = -OTU_ID, names_to = "KO_ID", values_to = "abundance") %>%
group_by(OTU_ID, KO_ID) %>%
summarize(abundance = sum(abundance)) %>%
ungroup()
# 根据KO ID获取KEGG通路信息
ko_to_pathway_file <- "picrust2_output/pathways_out/predicted_metagenome_unstratified.tsv.gz"
ko_to_pathway_table <- read.delim(ko_to_pathway_file, header = TRUE, stringsAsFactors = FALSE, check.names = FALSE)
colnames(ko_to_pathway_table) <- c("KO_ID", "pathway_ID", "abundance")
```
处理完毕后,就可以使用R中的其它工具对预测的功能注释数据进行进一步分析和可视化。