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AmpliconR项目介绍

​ 本项目主要用于对扩增子测序数据经qiime2分析产生的ASV丰度数据及其分类注释结果进行下游分析和结果可视化。

[TOC]

主要分析内容

01 ASV丰度数据过滤 [01Amplicon_rawdate_filter_stats.R]
	(包括非细菌、线粒体、叶绿体及样本重复性差的ASV过滤[min_reads >=10, min_samples >=3])
02 稀疏曲线 (rarecurve)数据分析及可视化 [02Amplicon_rarecurve.R]
03 数据归一化 (Flatten or CSS) [03Amplicon_Flatten_or_CSS.R]
04 alpha多样性分析 [04Amplicon_alpha_diversity.R]
05 beta多样性分析 [05Amplicon_beta_diversity.R]
06 Taxonomy表格整理 [06Split_taxonomy.R]
07 物种组成 (各分类水平相对丰度表格生成) [07Amplicon_relative_abundance.R]

其他：
ASV丰度数据拆分 [Split_ASV_by_metadata.R]
分组信息表格拆分 [Split_metadata.R]
alpha多样性组间差异分析 [Alpha_diversity_diff_factor1.R] 
                     [Alpha_diversity_diff_factor2.R]

环境依赖

​ 本流程依赖的 R 包可在 R version 4.4.2 下正常运行。

目录结构描述

./AmpliconR/
├── ReadMe.md
├── ReadMe.pdf
├── AmpliconR_pipeline.R
├── asv.table.txt
├── asv.taxa.txt
├── sample_info.txt
├── Rscript/
│   ├── 01Amplicon_rawdate_filter_stats.R
│   ├── 02Amplicon_rarecurve.R
│   ├── 03Amplicon_Flatten_or_CSS.R
│   ├── 04Amplicon_alpha_diversity_calculate.R
│   ├── 05Amplicon_beta_diversity.R
│   ├── 06Split_taxonomy.R
│   ├── 07Amplicon_relative_abundance.R
│   ├── Split_ASV_by_metadata.R
│   ├── Split_metadata.R
│   ├── Alpha_diversity_diff_factor1.R
│   └── Alpha_diversity_diff_factor2.R
├── results/
└── PlotScript/
    ├── 02Amplicon_rarecurve_plot.R
    ├── 04Amplicon_alpha_factor1_plot.R
    ├── 04Amplicon_alpha_factor2_plot.R
    └── 05Amplicon_beta_plot.R

使用说明

1、下载分析流程脚本

Github下载链接：https://github.com/shuailuo2021/AmpliconR.git

2、工作路径设置

setwd("yourpath/AmpliconR/")

# 新建 results 文件夹
new_folder <- "results"
if (!dir.exists(new_folder)) {
  dir.create(new_folder)
} else {
  print("文件夹已存在。")
}

3、输入文件格式要求

3.1 ASV丰度数据

ASV_table

注：ASV丰度数据表每行为ASV序号名，每列为样本编号。
要求ASV丰度数据表的第一列列名为“ASV_ID”,与以下分类注释表的第一列列名一致

3.2 ASV分类注释数据（Taxonomy）

Taxonomy

注：ASV分类注释表每行为ASV序号名及对应的分类注释信息和相似度。
要求ASV分类注释表的第一列列名为“ASV_ID”,与以上ASV丰度数据表的第一列列名一致

3.3 分组信息数据

sample_info

注：样本分组信息表每行为样本名称及对应的分组，具体根据自身情况而定。
要求样本分组信息表的第一列列名必须为“SampleID”。

4、数据分析流程

调用相应分析脚本 source(“./scripts/***.R”)
具体使用方法见 ./AmpliconR_pipeline.R

4.1 ASV数据过滤

# 调用脚本
source("./Rscript/01Amplicon_rawdate_filter_stats.R")

result <- process_asv_data_with_stats(
  asv_file_path = “./asv.table.txt",
  tax_file_path = "./asv.taxa.txt",
  min_reads = 10,
  min_samples = 3,
  output_dir = "./results/"
)

该脚本主要用于对原始ASV数据表进行过滤，包括：

  • 去除非细菌ASV（保留分类注释含"d__Bacteria"的ASV）
  • 去除分类注释含线粒体（Mitochondria）、叶绿体（Chloroplast）的ASV
  • 保留至少在min_samples（3）个样本中reads数达到min_reads（10）的ASV

结果文件：

  • ./results/ASV_keep.txt 过滤后ASV table
  • ./results/taxonomy_keep.txt 过滤后taxonomy
  • ./results/01processing_stats.txt 过滤过程统计信息

01processing_stats

4.2 rarecurve (稀疏曲线)

# 调用脚本
source("./scripts/02Amplicon_rarecurve.R")

# 自动调整横坐标范围
rare_plot <- plot_rare_curve(
  ASV_table_path = "./results/ASV_keep.txt",
  sample_info_path = "./sample_info.txt",
  color_var = "Niche",
  group_var = "Grazing",
  shape_var = "Grazing",
  output_path = "./results/02rarecurve_auto_range.pdf"
)

# 手动指定横坐标范围
rare_plot_custom <- plot_rare_curve(
  ASV_table_path = "./results/ASV_keep.txt",
  sample_info_path = "sample_info.txt",
  group_var = "Grazing",
  color_var = "Niche",
  shape_var = "Grazing",
  x_breaks = seq(0, 250000, by = 30000),
  output_path = "./results/02rarecurve_custom_range.pdf"
)

以上可通过默认设置和手动指定横坐标范围，二选一

根据稀疏曲线结果可用于确定数据归一化方法和抽平值

结果文件：

  • 02plot_richness_stat_meta.txt 结果文件可用于自定义绘图，可参考./PlotScript/02Amplicon_rarecurve_plot.R
  • 02rarecurve_auto_range.pdf 默认输出图片

02rarecurve_auto_range.pdf

4.3 数据归一化

4.3.1 数据抽平

# 调用脚本
source("./scripts/03Amplicon_Flatten_or_CSS.R")

# flatten归一化示例
flatten_result <- normalize_flatten(
  input_file = "./results/ASV_keep.txt",
  output_file = "./results/ASV_keep_norm.txt",
  min_reads = 10,
  min_samples = 3,
  seed = 123
)

# 查看处理过程信息
attributes(flatten_result)$processing_info

03Amplicon_Flatten

结果文件：

  • ./results/ASV_keep_norm.txt

4.3.2 CSS归一化 (cumulative sum scaling)

# 调用脚本
source("./scripts/03Amplicon_Flatten_or_CSS.R")

css_result <- normalize_CSS(
  input_file = "./results/ASV_keep.txt",
  output_file = "./results/ASV_keep_norm_CSS.txt",
  min_reads = 10,
  min_samples = 3
)

# 查看处理过程信息
attributes(css_result)$processing_info

03Amplicon_CSS

结果文件：

  • ./results/ASV_keep_norm_CSS.txt

4.4 Alpha多样性

# 调用脚本
source("./scripts/04Amplicon_alpha_diversity.R")

calculate_alpha_diversity(
  sample_info_file = "sample_info.txt",
  asv_norm_file = "./results/ASV_keep_norm.txt",
  output_file = "./results/04alpha_diversity.txt"
)

结果文件：

  • ./results/04alpha_diversity.txt

4.5 Beta多样性

# 调用脚本
source("./scripts/05Amplicon_beta_diversity.R")

beta_diversity_analysis(
  norm_asv_path = "./results/ASV_keep_norm.txt",
  sample_info_path = "sample_info.txt",
  output_dir = “./results/beta_all/"
)

结果文件：

  • ./results/beta_all/05beta_pcoa.txt beta多样性结果文件
  • ./results/beta_all/05pcoa_eigenvalues.txt 特征值解释度

结果文件可用于自定义绘图，可参考./PlotScript/05Amplicon_beta_plot.R

4.6 Taxonomy格式转换

# 调用脚本
source("./scripts/06Split_taxonomy.R")

# 保存文件并返回数据框
result_df <- process_taxonomy_table(
  input_file = "./results/taxonomy_keep.txt",
  output_file = "./results/taxonomy_split.txt"
)

结果文件./results/taxonomy_split.txt

taxonomy_split

4.7 物种组成 (各分类水平相对丰度表格生成)

# 调用脚本
source("./scripts/07Amplicon_relative_abundance.R")

# 完整用法（保存文件到指定目录）
result <- calculate_relative_abundance(
  taxonomy_file = "./results/taxonomy_split.txt",
  abundance_file = "./results/ASV_keep_norm.txt",
  output_dir = "./results/06relative_abundance"
)

# 从RDS文件重新加载结果
RA_results <- readRDS("./results/06relative_abundance/Relative_Abundance_Results.rds")
Phylum_RA_results <- RA_results$Phylum

结果文件：

Relative_Abundance

5、其他功能

5.1 ASV丰度数据表拆分

# 调用脚本
source("./scripts/Split_ASV_by_metadata.R")

# Split by Niche
split_asv_by_metadata(
  meta_file = "./sample_info.txt",
  asv_file = "./results/ASV_keep.txt",
  group_column = "Niche"
)

如根据生态位拆分ASV table的结果文件：

./AmpliconR/results/ASV_keep_Niche
├── ASV_keep_SO.txt
├── ASV_keep_RH.txt
└── ASV_keep_EC.txt

5.2 分组信息表格拆分

# 调用脚本
source("./scripts/Split_metadata.R")

result <- split_metadata(
  input_file = "./sample_info.txt",
  output_file = "./sample_info_split.rds",
  group_vars <- c("Niche","Grazing","NG")
)

根据分组变量拆分的结果文件：./sample_info_split.rds

# 从RDS文件读取结果
MetaSplit <- readRDS("./sample_info_split.rds")

# 提取特定分组数据
meta_SO <- MetaSplit$Niche$SO
meta_CK <- MetaSplit$Grazing$CK
meta_SO_CK <- MetaSplit$NG$SO_CK

5.3 Alpha多样性差异分析

5.3.1 单分组变量

# 调用脚本
source("./scripts/Alpha_diversity_diff_factor1.R")

# Shannon
result <- alpha_diversity_stats(
  input_path = "./results/04alpha_diversity.txt",
  output_path = "./results/04alpha_stats_Niche_Shannon.txt",
  group_column = “Niche”,      # 分组列名
  value_column = "Shannon"     # 数值列名
)

# Chao1
result <- alpha_diversity_stats(
  input_path = "./results/04alpha_diversity.txt",
  output_path = "./results/04alpha_stats_Niche_Chao1.txt",
  group_column = "Niche",      # 分组列名
  value_column = "S.chao1"     # 数值列名
)

结果文件：

  • ./results/04alpha_stats_Niche_Shannon.txt Shannon组间差异分析结果
  • ./results/beta_all/04alpha_stats_Niche_Chao1.txt Chao1组间差异分析结果

diff_alpha_Niche_Shannon

结果文件可用于自定义绘图，可参考./PlotScript/04Amplicon_alpha_factor1_plot.R

5.3.2 双分组变量

# 调用脚本
source("./scripts/Alpha_diversity_diff_factor2.R")

# Shannon
result <- kruskal_group_comparison(
  input_file = "./results/04alpha_diversity.txt",
  output_file = "./results/04alpha_kruskal_results_Shannon.txt",
  group_var = "Niche",
  compare_var = "Grazing",
  value_var = "Shannon"
)

# Chao1
result <- kruskal_group_comparison(
  input_file = "./results/04alpha_diversity.txt",
  output_file = "./results/04alpha_kruskal_results_Chao1.txt",
  group_var = "Niche",
  compare_var = "Grazing",
  value_var = "S.chao1"
)

结果文件：

  • ./results/04alpha_kruskal_results_Shannon.txt Shannon组间差异分析结果
  • ./results/04alpha_kruskal_results_Chao1.txt Chao1组间差异分析结果

diff_alpha_kruskal_Shannon

结果文件可用于自定义绘图，可参考./PlotScript/04Amplicon_alpha_factor2_plot.R

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