amplysis

An R package for rapid analysis of 16S amplicon sequencing data

16S 扩增子测序数据分析 R 包

An R package for 16S rRNA gene amplicon sequencing data that integrates data preprocessing, analysis, and visualization methods. These methods include microbial composition analysis, α-diversity analysis, β-diversity analysis, differential analysis, correlation analysis, and network analysis, among others.

一个用于 16S rRNA 基因扩增子测序数据，集成了数据预处理、分析与可视化方法的 R 包。这些方法包括微生物组成分析、α 多样性分析、β 多样性分析、差异分析、关联分析和网络分析等。

Background 背景

The rapid development of bioinformatics tools has enabled researchers to perform complex 16S rRNA gene amplicon sequencing analysis. However, for those without a bioinformatics background, existing tools and R packages can be complex and difficult to use. The amplysis project was created to address this issue, providing an accessible solution for researchers to easily conduct data analysis.

生物信息学工具的快速发展，使得研究者能够进行复杂的 16S rRNA 基因扩增子测序数据分析。然而，对于缺乏生物信息学背景的研究者来说，现有的工具和 R 包操作复杂，使用门槛较高。amplysis 集合 R 包项目应运而生，旨在为研究者提供一个易于使用的数据分析解决方案。

Install 安装

Install from CRAN

从 CRAN 安装

Important

Submitting to CRAN, stay tuned...

正在向 CRAN 提交中，敬请期待...

install.packages("amplysis")

Install the latest version from GitHub (recommended)

从 Github 上安装最新版本（推荐）

安装 R 包 devtools：

install.packages("devtools")
library(devtools)

通过 devtools 安装 R 包 amplysis：

devtools::install_github("min-perilla/amplysis")

Usage 使用方法

示例数据下载链接

# 清除所有变量 | Clear all variables
rm(list = ls())

# 加载 amplysis | Load amplysis package
library(amplysis)

# 设置工作目录 | Set working directory
set_wd()

# 加载示例数据 | Load example data
otu = read_data("otu.csv")            # 特征表 | OTU table
tax = read_data("tax.csv")            # 分类表 | Taxonomy table
metadata = read_data("metadata.csv")  # 样本元数据 | Sample metadata
rep = read_data("rep_seqs.csv")       # 代表性序列 | Representative sequences
env = read_data("env.csv")            # 环境因子 | Environmental factors
tree = read_data("tree_rooted.nwk")   # 系统发育树 | Phylogenetic tree

# 数据预处理：分类表 | Data preprocessing: Taxonomy table
tax = tax_separate(tax = tax, index = 2, delim = "; ")                     # 分类表数据分列 | Split taxonomy table into columns
tax = tax_trim_prefix(tax = tax, index = c(2:8), length = 3)               # 分类表去前缀 | Remove prefixes from taxonomy table
tax = tax_names_repair(tax = tax, column_to_check = 7, column_to_add = 3)  # 分类表信息修复 | Repair taxonomy table names

# 数据预处理：数据抽平 | Data preprocessing: Rarefaction
otu_tax = data_rarefy(otu, method = "phyloseq", tax_table = tax)

# 拆分特征表和分类表 | Separate OTU table and taxonomy table
otu = otu_tax[["otu"]]
tax = otu_tax[["tax"]]

# 对齐代表性序列文件 | Align representative sequences file
otu_rep = merge(x = otu, y = rep, by = "#OTU ID", all.x = T, sort = F)

# 拆分特征表和代表性序列文件 | Separate OTU table and representative sequences
rep = otu_rep[, c(1, ncol(otu_rep))]
otu = otu_rep[, -c(ncol(otu_rep))]

# -----------------------------------------
# 数据分析与可视化 | Data Analysis & Visualization

# 物种堆叠图分析（门水平） | Stacked bar plot analysis (Phylum level)
data_sta_p = stackbar(otu, tax, metadata, tax_cla = "phylum", group1 = "group", group2 = "group2", row_n = 8)
# 可视化 | Visualization
stackbar_plot(data_sta_p, tax_cla = "phylum", title_legend = "Top 8 Phyla")

# 物种堆叠图分析（属水平） | Stacked bar plot analysis (Genus level)
data_sta_g = stackbar(otu, tax, metadata, tax_cla = "genus", group1 = "group", group2 = "group2", row_n = 20)
# 可视化 | Visualization
stackbar_plot(data_sta_g, tax_cla = "genus", title_legend = "Top 20 Genera")

# 弦图（门水平） | Chord diagram (Phylum level)
data_chord = chord(otu, metadata, tax, tax_cla = "phylum", group = "group2", row_n = 8)
# 可视化 | Visualization
chord_plot(data_chord)

# 韦恩图 | Venn diagram
data_venn = venn(otu, metadata, group = "group2")
# 可视化 | Visualization
venn_plot(data_venn)

# 集合图 | Upset plot
data_upset = Upset(otu, metadata, group = "group2")
# 可视化 | Visualization
Upset_plot(data_upset)

# 箱线图（Alpha 多样性分析） | Boxplot (Alpha diversity analysis)
data_alpha = alpha(otu, metadata, group = "group2", tree = tree)
# 可视化 | Visualization
alpha_plot(data_alpha)

# PCA | Principal Component Analysis (PCA)
data_pca = pca(otu, metadata, group = "group2")
# 可视化 | Visualization
pca_plot(data_pca)

# PCoA | Principal Coordinates Analysis (PCoA)
data_pcoa = pcoa(otu, metadata, group = "group2")
# 可视化 | Visualization
pcoa_plot(data_pcoa)

# NMDS | Non-metric Multidimensional Scaling (NMDS)
data_nmds = nmds(otu, metadata, group = "group2")
# 可视化 | Visualization
nmds_plot(data_nmds)

# RDA | Redundancy Analysis (RDA)
data_rda = RDA(otu, env, metadata, group = "group2")
# 可视化 | Visualization
RDA_plot(data_rda)

# CCA | Canonical Correspondence Analysis (CCA)
data_cca = CCA(otu, env, metadata, group = "group2")
# 可视化 | Visualization
CCA_plot(data_cca)

# 热图 | Heatmap
data_heatmap = heatmap(otu, tax, metadata, tax_cla = "genus", group1 = "group", group2 = "group2", row_n = 30)
# 可视化 | Visualization
heatmap_plot(data_heatmap, fontsize_col = 14, file_height = 10, file_width = 12)

# 共现性网络分析 | Co-occurrence network analysis
data_net = network(otu, tax, metadata, tax_cla = "genus")
data_net

Partial example figures:

部分示例图：

Maintainers 项目主要负责人

@min-perilla

Contributing 贡献

@min-perilla

License 许可证

The project is licensed under GPL (>= 3). For more details, please refer to the LICENSE file.

该项目采用 GPL (>= 3) 许可证，详情请参阅 LICENSE 文件。