文章MSM_metagenomics（三）：Alpha多样性分析

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介绍

本教程使用基于R的函数来估计微生物群落的香农指数和丰富度，使用MetaPhlAn profile数据。估计结果进一步进行了可视化，并与元数据关联，以测试统计显著性。

数据

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R 包

• SummarizedExperiment
• mia
• ggpubr
• ggplot2
• lfe

Alpha diversity estimation and visualization

使用alpha_diversity_funcs.R计算alpha多样性和可视化。

• 代码

SE_converter <- function(md_rows, tax_starting_row, mpa_md) {# SE_converter function is to convery metadata-wedged mpa table into SummarisedExperiment structure.# md_rows: a vector specifying the range of rows indicating metadata.# tax_starting_row: an interger corresponding to the row where taxonomic abundances start.# mpa_md: a metaphlan table wedged with metadata, in the form of dataframe.md_df <- mpa_md[md_rows,] # extract metadata part from mpa_md tabletax_df <- mpa_md[tax_starting_row: nrow(mpa_md),] # extract taxonomic abundances part from mpa_md table### convert md_df to a form compatible with SummarisedExperiment ### SE_md_df <- md_df[, -1]rownames(SE_md_df) <- md_df[, 1]SE_md_df <- t(SE_md_df)### convert md_df to a form compatible with SummarisedExperiment ###### prep relab values in a form compatible with SummarisedExperiment ###SE_relab_df <- tax_df[, -1]rownames(SE_relab_df) <- tax_df[, 1]col_names <- colnames(SE_relab_df)SE_relab_df[, col_names] <- apply(SE_relab_df[, col_names], 2, function(x) as.numeric(as.character(x)))### prep relab values in a form compatible with SummarisedExperiment ###SE_tax_df <- tax_df[, 1:2]rownames(SE_tax_df) <- tax_df[, 1]SE_tax_df <- SE_tax_df[-2]colnames(SE_tax_df) <- c("species")SE_data <- SummarizedExperiment::SummarizedExperiment(assays = list(relative_abundance = SE_relab_df),colData = SE_md_df,rowData = SE_tax_df)SE_data
}est_alpha_diversity <- function(se_data) {# This function is to estimate alpha diversity (shannon index and richness) of a microbiome and output results in dataframe.# se_data: the SummarizedExperiment data structure containing metadata and abundance values.se_data <- se_data |>mia::estimateRichness(abund_values = "relative_abundance", index = "observed")se_data <- se_data |>mia::estimateDiversity(abund_values = "relative_abundance", index = "shannon")se_alpha_div <- data.frame(SummarizedExperiment::colData(se_data))se_alpha_div
}make_boxplot <- function(df, xlabel, ylabel, font_size = 11, jitter_width = 0.2, dot_size = 1, font_style = "Arial", stats = TRUE, pal = NULL) {# This function is to create a boxplot using categorical data.# df: The dataframe containing microbiome alpha diversities, e.g. `shannon` and `observed` with categorical metadata.# xlabel: the column name one will put along x-axis.# ylabel: the index estimate one will put along y-axis.# font_size: the font size, default: [11]# jitter_width: the jitter width, default: [0.2]# dot_size: the dot size inside the boxplot, default: [1]# font_style: the font style, default: `Arial`# pal: a list of color codes for pallete, e.g. c(#888888, #eb2525). The order corresponds the column order of boxplot.# stats: wilcox rank-sum test. default: TRUEif (stats) {nr_group = length(unique(df[, xlabel])) # get the number of groupsif (nr_group == 2) {group_pair = list(unique(df[, xlabel]))ggpubr::ggboxplot(data = df, x = xlabel, y = ylabel, color = xlabel,palette = pal, ylab = ylabel, xlab = xlabel,add = "jitter", add.params = list(size = dot_size, jitter = jitter_width)) +ggpubr::stat_compare_means(comparisons = group_pair, exact = T, alternative = "less") +ggplot2::stat_summary(fun.data = function(x) data.frame(y = max(df[, ylabel]), label = paste("Mean=",mean(x))), geom="text") +ggplot2::theme(text = ggplot2::element_text(size = font_size, family = font_style))} else {group_pairs = my_combn(unique((df[, xlabel])))ggpubr::ggboxplot(data = df, x = xlabel, y = ylabel, color = xlabel,palette = pal, ylab = ylabel, xlab = xlabel,add = "jitter", add.params = list(size = dot_size, jitter = jitter_width)) +ggpubr::stat_compare_means() + ggpubr::stat_compare_means(comparisons = group_pairs, exact = T, alternative = "greater") +ggplot2::stat_summary(fun.data = function(x) data.frame(y= max(df[, ylabel]), label = paste("Mean=",mean(x))), geom="text") +ggplot2::theme(text = ggplot2::element_text(size = font_size, family = font_style))}} else {ggpubr::ggboxplot(data = df, x = xlabel, y = ylabel, color = xlabel,palette = pal, ylab = ylabel, xlab = xlabel,add = "jitter", add.params = list(size = dot_size, jitter = jitter_width)) +ggplot2::theme(text = ggplot2::element_text(size = font_size, family = font_style))}
}my_combn <- function(x) {combs <- list()comb_matrix <- combn(x, 2)for (i in 1: ncol(comb_matrix)) {combs[[i]]  <- comb_matrix[,i]}combs
}felm_fixed <- function(data_frame, f_factors, t_factor, measure) {# This function is to perform fixed effect linear modeling# data_frame: a dataframe containing measures and corresponding effects  # f_factors: a vector of header names in the dataframe which represent fixed effects# t_factors: test factor name in the form of string# measure: the measured values in column, e.g., shannon or richness
#   all_factors <- c(t_factor, f_factors)
#   for (i in all_factors) {
#     vars <- unique(data_frame[, i])
#     lookup <- setNames(seq_along(vars) -1, vars)
#     data_frame[, i] <- lookup[data_frame[, i]]
#   }
#   View(data_frame)str1 <- paste0(c(t_factor, paste0(f_factors, collapse = " + ")), collapse = " + ")str2 <- paste0(c(measure, str1), collapse = " ~ ")felm_stats <- lfe::felm(eval(parse(text = str2)), data = data_frame)felm_stats
}

加载一个包含元数据和分类群丰度的合并MetaPhlAn profile文件

mpa_df <- data.frame(read.csv("./data/merged_abundance_table_species_sgb_md.tsv", header = TRUE, sep = "\t"))

sample	P057	P054	P052	…	P049
sexual_orientation	MSM	MSM	MSM	…	Non-MSM
HIV_status	negative	positive	positive	…	negative
ethnicity	Caucasian	Caucasian	Caucasian	…	Caucasian
antibiotics_6month	Yes	No	No	…	No
BMI_kg_m2_WHO	ObeseClassI	Overweight	Normal	…	Overweight
Methanomassiliicoccales_archaeon	0.0	0.0	0.0	…	0.01322
…	…	…	…	…	…
Methanobrevibacter_smithii	0.0	0.0	0.0	…	0.19154

接下来，我们将数据框转换为SummarizedExperiment数据结构，以便使用SE_converter函数继续分析，该函数需要指定三个参数：

• md_rows: a vector specifying the range of rows indicating metadata. Note: 1-based.
• tax_starting_row: an interger corresponding to the row where taxonomic abundances start.
• mpa_md: a metaphlan table wedged with metadata, in the form of dataframe.

SE <- SE_converter(md_rows = 1:5,tax_starting_row = 6, mpa_md = mpa_df)SE                   class: SummarizedExperiment
dim: 1676 139
metadata(0):
assays(1): relative_abundance
rownames(1676): Methanomassiliicoccales_archaeon|t__SGB376GGB1567_SGB2154|t__SGB2154 ... Entamoeba_dispar|t__EUK46681Blastocystis_sp_subtype_1|t__EUK944036
rowData names(1): species
colnames(139): P057 P054 ... KHK16 KHK11
colData names(5): sexual_orientation HIV_status ethnicityantibiotics_6month BMI_kg_m2_WHO

接下来，我们可以使用est_alpha_diversity函数来估计每个宏基因组样本的香农指数和丰富度。

alpha_df <- est_alpha_diversity(se_data = SE)
alpha_df

	sexual_orientation	HIV_status	ethnicity	antibiotics_6month	BMI_kg_m2_WHO	observed	shannon
P057	MSM	negative	Caucasian	Yes	ObeseClassI	134	3.1847
P054	MSM	positive	Caucasian	No	Overweight	141	2.1197
…	…	…	…	…	…	…	…
P052	MSM	positive	Caucasian	No	Normal	152	2.5273

为了比较不同组之间的alpha多样性差异，我们可以使用make_boxplot函数，并使用参数：

• df: The dataframe containing microbiome alpha diversities, e.g. shannon and observed with categorical metadata.
• xlabel: the column name one will put along x-axis.
• ylabel: the index estimate one will put along y-axis.
• font_size: the font size, default: [11]
• jitter_width: the jitter width, default: [0.2]
• dot_size: the dot size inside the boxplot, default: [1]
• font_style: the font style, default: Arial
• pal: a list of color codes for pallete, e.g. c(#888888, #eb2525). The order corresponds the column order of boxplot.
• stats: wilcox rank-sum test. default: TRUE

shannon <- make_boxplot(df = alpha_df,xlabel = "sexual_orientation",ylabel = "shannon",stats = TRUE,pal = c("#888888", "#eb2525"))richness <- make_boxplot(df = alpha_df,xlabel = "sexual_orientation", ylabel = "observed",stats = TRUE,pal = c("#888888", "#eb2525"))
multi_plot <- ggpubr::ggarrange(shannon, richness, ncol = 2)
ggplot2::ggsave(file = "shannon_richness.svg", plot = multi_plot, width = 4, height = 5)

通过固定效应线性模型估计关联的显著性

在宏基因组分析中，除了感兴趣的变量（例如性取向）之外，通常还需要处理多个变量（例如HIV感染和抗生素使用）。因此，在测试微生物群落矩阵（例如香农指数或丰富度）与感兴趣的变量（例如性取向）之间的关联时，控制这些混杂效应非常重要。在这里，我们使用基于固定效应线性模型的felm_fixed函数，该函数实现在R包lfe 中，以估计微生物群落与感兴趣变量之间的关联显著性，同时控制其他变量的混杂效应。

• data_frame: The dataframe containing microbiome alpha diversities, e.g. shannon and observed with multiple variables.
• f_factors: A vector of variables representing fixed effects.
• t_factor: The variable of interest for testing.
• measure: The header indicating microbiome measure, e.g. shannon or richness

lfe_stats <- felm_fixed(data_frame = alpha_df,f_factors = c(c("HIV_status", "antibiotics_6month")),t_factor = "sexual_orientation",measure = "shannon")
summary(lfe_stats)
Residuals:Min      1Q  Median      3Q     Max 
-2.3112 -0.4666  0.1412  0.5200  1.4137 Coefficients:Estimate Std. Error t value Pr(>|t|)    
(Intercept)            3.62027    0.70476   5.137 9.64e-07 ***
sexual_orientationMSM  0.29175    0.13733   2.125   0.0355 *  
HIV_statuspositive    -0.28400    0.14658  -1.937   0.0548 .  
antibiotics_6monthNo  -0.10405    0.67931  -0.153   0.8785    
antibiotics_6monthYes  0.01197    0.68483   0.017   0.9861    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1Residual standard error: 0.6745 on 134 degrees of freedom
Multiple R-squared(full model): 0.07784   Adjusted R-squared: 0.05032 
Multiple R-squared(proj model): 0.07784   Adjusted R-squared: 0.05032 
F-statistic(full model):2.828 on 4 and 134 DF, p-value: 0.02725 
F-statistic(proj model): 2.828 on 4 and 134 DF, p-value: 0.02725