Diversity Analysis

Published

Jun 2026

ID: MICROB-008
Type: System Component
Audience: Students, researchers, analysts, and practitioners
Theme: Comparing microbial diversity within and between samples

Introduction

Diversity analysis is the stage where microbiome communities are compared within samples and between samples.

After feature generation and taxonomic profiling, the analysis contains structured feature abundance tables and taxonomic summaries. Diversity analysis uses these tables to evaluate microbial richness, evenness, and community dissimilarity.

Diversity analysis is often one of the central components of microbiome reporting because it provides a compact way to describe community structure.

Why Diversity Analysis Matters

Diversity analysis helps answer questions such as:

How many microbial features are observed in each sample?
Are some samples more diverse than others?
Are microbial communities similar or different across samples?
Do groups separate based on community composition?
Are observed patterns consistent with metadata or study design?

Diversity analysis provides both descriptive and comparative views of microbial communities.

Position in the Microbiome Analysis System

Diversity analysis occurs after feature generation and taxonomic profiling.

Show code

flowchart LR
  A[Feature Table] --> B[Diversity Analysis]
  C[Sample Metadata] --> B
  B --> D[Biological Interpretation]
  B --> E[Reproducible Reporting]

flowchart LR
  A[Feature Table] --> B[Diversity Analysis]
  C[Sample Metadata] --> B
  B --> D[Biological Interpretation]
  B --> E[Reproducible Reporting]

Diversity results should always be interpreted together with study design, metadata, sequencing depth, feature generation choices, and quality control decisions.

Alpha Diversity

Alpha diversity describes diversity within a sample.

Common alpha diversity measures include:

observed features
Shannon diversity
Simpson diversity
Faith’s phylogenetic diversity
evenness

In this lightweight MAS example, we calculate two simple alpha diversity metrics:

observed features
Shannon diversity

Observed features count how many features have nonzero abundance in each sample.

Shannon diversity incorporates both richness and evenness.

Beta Diversity

Beta diversity describes differences between samples.

Common beta diversity measures include:

Bray-Curtis dissimilarity
Jaccard distance
UniFrac distances
Aitchison distance

In this lightweight MAS example, we calculate a simple Bray-Curtis dissimilarity matrix from the toy feature table.

Bray-Curtis dissimilarity compares differences in abundance between pairs of samples.

Interpretation Cautions

Diversity metrics are useful summaries, but they are not conclusions by themselves.

Important cautions include:

sequencing depth can influence diversity estimates
rare feature filtering affects observed richness
different metrics emphasize different properties
beta diversity patterns may reflect batch effects
ordination plots can be visually persuasive but require careful interpretation
small sample sizes limit statistical confidence
toy example data should not be biologically interpreted

For real microbiome studies, diversity analysis should be paired with appropriate statistical testing and careful metadata review.

Example Diversity Analysis Scripts

The following scripts provide a lightweight MAS-side diversity analysis example.

The workflow uses two scripts:

scripts/R/08a-calculate-diversity-metrics.R
scripts/R/08b-plot-diversity-results.R

The first script calculates simple alpha diversity metrics and a Bray-Curtis distance matrix.

The second script creates simple alpha diversity and beta diversity plots.

08a: Calculate Diversity Metrics

Save this script as:

scripts/R/08a-calculate-diversity-metrics.R

###############################################################################
# Microbiome Analysis System
# 08a-calculate-diversity-metrics.R
#
# Purpose:
#   Calculate simple alpha and beta diversity metrics from the example
#   feature table.
#
# Usage:
#   Rscript scripts/R/08a-calculate-diversity-metrics.R
###############################################################################

library(readr)
library(dplyr)
library(tidyr)
library(tibble)

feature_dir <- "data/features"
metadata_dir <- "data/metadata"
diversity_dir <- "data/diversity"
report_dir <- "data/reports"

dir.create(diversity_dir, recursive = TRUE, showWarnings = FALSE)
dir.create(report_dir, recursive = TRUE, showWarnings = FALSE)

feature_table_file <- file.path(feature_dir, "feature-table.tsv")
sample_metadata_file <- file.path(metadata_dir, "sample-metadata.tsv")

if (!file.exists(feature_table_file)) {
  stop(
    "Missing feature table: ",
    feature_table_file,
    "\nRun: bash scripts/bash/06a-create-example-feature-table.sh"
  )
}

if (!file.exists(sample_metadata_file)) {
  stop(
    "Missing sample metadata: ",
    sample_metadata_file,
    "\nRun: bash scripts/bash/06a-create-example-feature-table.sh"
  )
}

feature_table <- read_tsv(feature_table_file, show_col_types = FALSE)
sample_metadata <- read_tsv(sample_metadata_file, show_col_types = FALSE)

feature_matrix <- feature_table %>%
  column_to_rownames("feature_id") %>%
  as.matrix()

sample_matrix <- t(feature_matrix)

shannon_index <- function(x) {
  total <- sum(x)
  if (total == 0) {
    return(0)
  }
  p <- x[x > 0] / total
  -sum(p * log(p))
}

alpha_diversity <- tibble(
  sample_id = rownames(sample_matrix),
  total_reads = rowSums(sample_matrix),
  observed_features = rowSums(sample_matrix > 0),
  shannon_diversity = apply(sample_matrix, 1, shannon_index)
) %>%
  left_join(sample_metadata, by = "sample_id")

write_tsv(
  alpha_diversity,
  file.path(diversity_dir, "alpha-diversity.tsv")
)

bray_curtis <- function(x, y) {
  numerator <- sum(abs(x - y))
  denominator <- sum(x + y)
  if (denominator == 0) {
    return(0)
  }
  numerator / denominator
}

sample_ids <- rownames(sample_matrix)
bray_matrix <- matrix(
  0,
  nrow = length(sample_ids),
  ncol = length(sample_ids),
  dimnames = list(sample_ids, sample_ids)
)

for (i in seq_along(sample_ids)) {
  for (j in seq_along(sample_ids)) {
    bray_matrix[i, j] <- bray_curtis(sample_matrix[i, ], sample_matrix[j, ])
  }
}

bray_table <- as.data.frame(bray_matrix) %>%
  rownames_to_column("sample_id")

write_tsv(
  bray_table,
  file.path(diversity_dir, "bray-curtis-distance-matrix.tsv")
)

pcoa <- cmdscale(as.dist(bray_matrix), k = 2, eig = TRUE)

ordination <- tibble(
  sample_id = rownames(pcoa$points),
  axis1 = pcoa$points[, 1],
  axis2 = pcoa$points[, 2]
) %>%
  left_join(sample_metadata, by = "sample_id")

write_tsv(
  ordination,
  file.path(diversity_dir, "bray-curtis-pcoa.tsv")
)

report <- tibble(
  metric = c(
    "sample_count",
    "feature_count",
    "alpha_metrics",
    "beta_metric",
    "ordination_method",
    "diversity_status"
  ),
  value = c(
    nrow(sample_matrix),
    ncol(sample_matrix),
    "observed_features; shannon_diversity",
    "Bray-Curtis",
    "PCoA using cmdscale",
    "READY_FOR_PLOTTING"
  )
)

write_tsv(
  report,
  file.path(report_dir, "diversity-analysis-report.tsv")
)

message("Created:")
message("  ", file.path(diversity_dir, "alpha-diversity.tsv"))
message("  ", file.path(diversity_dir, "bray-curtis-distance-matrix.tsv"))
message("  ", file.path(diversity_dir, "bray-curtis-pcoa.tsv"))
message("  ", file.path(report_dir, "diversity-analysis-report.tsv"))

Run it from the MAS project root:

Rscript scripts/R/08a-calculate-diversity-metrics.R

This creates:

data/diversity/alpha-diversity.tsv
data/diversity/bray-curtis-distance-matrix.tsv
data/diversity/bray-curtis-pcoa.tsv
data/reports/diversity-analysis-report.tsv

08b: Plot Diversity Results

Save this script as:

scripts/R/08b-plot-diversity-results.R

###############################################################################
# Microbiome Analysis System
# 08b-plot-diversity-results.R
#
# Purpose:
#   Plot simple alpha diversity and beta diversity results.
#
# Usage:
#   Rscript scripts/R/08b-plot-diversity-results.R
###############################################################################

library(readr)
library(dplyr)
library(ggplot2)

diversity_dir <- "data/diversity"
figure_dir <- "figures"
table_dir <- "tables"

dir.create(figure_dir, recursive = TRUE, showWarnings = FALSE)
dir.create(table_dir, recursive = TRUE, showWarnings = FALSE)

alpha_file <- file.path(diversity_dir, "alpha-diversity.tsv")
ordination_file <- file.path(diversity_dir, "bray-curtis-pcoa.tsv")

if (!file.exists(alpha_file)) {
  stop(
    "Missing alpha diversity file: ",
    alpha_file,
    "\nRun: Rscript scripts/R/08a-calculate-diversity-metrics.R"
  )
}

if (!file.exists(ordination_file)) {
  stop(
    "Missing ordination file: ",
    ordination_file,
    "\nRun: Rscript scripts/R/08a-calculate-diversity-metrics.R"
  )
}

alpha <- read_tsv(alpha_file, show_col_types = FALSE)
ordination <- read_tsv(ordination_file, show_col_types = FALSE)

write_tsv(
  alpha,
  file.path(table_dir, "alpha-diversity-for-plot.tsv")
)

write_tsv(
  ordination,
  file.path(table_dir, "bray-curtis-pcoa-for-plot.tsv")
)

p_alpha <- ggplot(
  alpha,
  aes(
    x = sample_id,
    y = shannon_diversity
  )
) +
  geom_col() +
  labs(
    title = "Example Shannon Diversity",
    subtitle = "Toy MAS example data for workflow testing",
    x = "Sample",
    y = "Shannon diversity"
  ) +
  theme_minimal(base_size = 12) +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1)
  )

ggsave(
  filename = file.path(figure_dir, "alpha-shannon-diversity.png"),
  plot = p_alpha,
  width = 7,
  height = 5,
  dpi = 300
)

p_beta <- ggplot(
  ordination,
  aes(
    x = axis1,
    y = axis2,
    label = sample_id
  )
) +
  geom_point(size = 3) +
  geom_text(vjust = -0.8) +
  labs(
    title = "Example Bray-Curtis PCoA",
    subtitle = "Toy MAS example data for workflow testing",
    x = "PCoA axis 1",
    y = "PCoA axis 2"
  ) +
  theme_minimal(base_size = 12)

ggsave(
  filename = file.path(figure_dir, "bray-curtis-pcoa.png"),
  plot = p_beta,
  width = 7,
  height = 5,
  dpi = 300
)

message("Created:")
message("  ", file.path(table_dir, "alpha-diversity-for-plot.tsv"))
message("  ", file.path(table_dir, "bray-curtis-pcoa-for-plot.tsv"))
message("  ", file.path(figure_dir, "alpha-shannon-diversity.png"))
message("  ", file.path(figure_dir, "bray-curtis-pcoa.png"))

Run it from the MAS project root:

Rscript scripts/R/08b-plot-diversity-results.R

This creates:

tables/alpha-diversity-for-plot.tsv
tables/bray-curtis-pcoa-for-plot.tsv
figures/alpha-shannon-diversity.png
figures/bray-curtis-pcoa.png

Running the Complete Diversity Example

If you are continuing from Chapter 06 or 07, first make sure the example feature table exists:

bash scripts/bash/06a-create-example-feature-table.sh
bash scripts/bash/06b-check-feature-table.sh

Then calculate and plot diversity results:

Rscript scripts/R/08a-calculate-diversity-metrics.R
Rscript scripts/R/08b-plot-diversity-results.R
cat data/reports/diversity-analysis-report.tsv
cat data/diversity/alpha-diversity.tsv

The toy example contains only three samples and five features. This is enough to test workflow structure, but it is not enough for real statistical interpretation.

Example Alpha Diversity Output

The generated alpha diversity table may look like this:

sample_id   total_reads observed_features   shannon_diversity   group   sample_type description
SRR17868090 185 4   0.978   healthy stool   toy example sample 1
SRR17868091 169 4   1.253   healthy stool   toy example sample 2
SRR17868092 186 5   1.417   healthy stool   toy example sample 3

Values will depend on the feature table used.

Example Beta Diversity Output

The Bray-Curtis distance matrix summarizes pairwise community dissimilarity.

sample_id   SRR17868090 SRR17868091 SRR17868092
SRR17868090 0.000   0.231   0.542
SRR17868091 0.231   0.000   0.328
SRR17868092 0.542   0.328   0.000

Lower values indicate more similar samples. Higher values indicate more different samples.

Interpreting Diversity Results

Diversity results should be interpreted in relation to:

sample type
sequencing depth
feature generation method
filtering decisions
biological group
metadata completeness
batch effects
study design
sample size

A diversity difference may be biologically interesting, but it should not be interpreted in isolation.

Common Diversity Analysis Issues

Common issues include:

comparing samples with very different sequencing depths
interpreting ordination separation without statistical support
ignoring batch effects
using metrics that do not match the biological question
overinterpreting small sample sizes
mixing datasets generated by different methods
failing to document filtering and normalization choices

These issues should be considered before reporting diversity findings.

MAS Diversity Outputs

At the end of this stage, MAS should have:

alpha diversity table
beta diversity distance matrix
ordination coordinates
diversity analysis report
alpha diversity plot
beta diversity plot
notes on diversity metrics and limitations

Show code

flowchart LR
  A[Feature Table] --> B[Alpha Diversity]
  A --> C[Beta Diversity]
  B --> D[Diversity Report]
  C --> D
  D --> E[Biological Interpretation]

flowchart LR
  A[Feature Table] --> B[Alpha Diversity]
  A --> C[Beta Diversity]
  B --> D[Diversity Report]
  C --> D
  D --> E[Biological Interpretation]

Key Takeaways

Diversity analysis summarizes microbial community structure.

A strong diversity analysis stage ensures that:

alpha diversity is calculated and interpreted carefully
beta diversity is linked to sample metadata
ordination plots are not overinterpreted
sequencing depth and feature filtering are considered
diversity metrics match the biological question
limitations are documented

Diversity analysis provides important evidence, but it is one part of the broader microbiome interpretation system.

What Comes Next

The next chapter examines Functional Profiling, where microbiome data are used to explore gene families, pathways, and microbial functional potential.

# Diversity Analysis :::cdi-message - **ID:** MICROB-008 - **Type:** System Component - **Audience:** Students, researchers, analysts, and practitioners - **Theme:** Comparing microbial diversity within and between samples ::: ## Introduction Diversity analysis is the stage where microbiome communities are compared within samples and between samples. After feature generation and taxonomic profiling, the analysis contains structured feature abundance tables and taxonomic summaries. Diversity analysis uses these tables to evaluate microbial richness, evenness, and community dissimilarity. Diversity analysis is often one of the central components of microbiome reporting because it provides a compact way to describe community structure. ## Why Diversity Analysis Matters Diversity analysis helps answer questions such as: - How many microbial features are observed in each sample? - Are some samples more diverse than others? - Are microbial communities similar or different across samples? - Do groups separate based on community composition? - Are observed patterns consistent with metadata or study design? Diversity analysis provides both descriptive and comparative views of microbial communities. ## Position in the Microbiome Analysis System Diversity analysis occurs after feature generation and taxonomic profiling. ```{mermaid} flowchart LR A[Feature Table] --> B[Diversity Analysis] C[Sample Metadata] --> B B --> D[Biological Interpretation] B --> E[Reproducible Reporting] ``` Diversity results should always be interpreted together with study design, metadata, sequencing depth, feature generation choices, and quality control decisions. ## Alpha Diversity Alpha diversity describes diversity within a sample. Common alpha diversity measures include: - observed features - Shannon diversity - Simpson diversity - Faith's phylogenetic diversity - evenness In this lightweight MAS example, we calculate two simple alpha diversity metrics: - observed features - Shannon diversity Observed features count how many features have nonzero abundance in each sample. Shannon diversity incorporates both richness and evenness. ## Beta Diversity Beta diversity describes differences between samples. Common beta diversity measures include: - Bray-Curtis dissimilarity - Jaccard distance - UniFrac distances - Aitchison distance In this lightweight MAS example, we calculate a simple Bray-Curtis dissimilarity matrix from the toy feature table. Bray-Curtis dissimilarity compares differences in abundance between pairs of samples. ## Interpretation Cautions Diversity metrics are useful summaries, but they are not conclusions by themselves. Important cautions include: - sequencing depth can influence diversity estimates - rare feature filtering affects observed richness - different metrics emphasize different properties - beta diversity patterns may reflect batch effects - ordination plots can be visually persuasive but require careful interpretation - small sample sizes limit statistical confidence - toy example data should not be biologically interpreted For real microbiome studies, diversity analysis should be paired with appropriate statistical testing and careful metadata review. ## Example Diversity Analysis Scripts The following scripts provide a lightweight MAS-side diversity analysis example. The workflow uses two scripts: ```text scripts/R/08a-calculate-diversity-metrics.R scripts/R/08b-plot-diversity-results.R ``` The first script calculates simple alpha diversity metrics and a Bray-Curtis distance matrix. The second script creates simple alpha diversity and beta diversity plots. ## 08a: Calculate Diversity Metrics Save this script as: ```text scripts/R/08a-calculate-diversity-metrics.R ``` ```r ############################################################################### # Microbiome Analysis System # 08a-calculate-diversity-metrics.R # # Purpose: # Calculate simple alpha and beta diversity metrics from the example # feature table. # # Usage: # Rscript scripts/R/08a-calculate-diversity-metrics.R ############################################################################### library(readr) library(dplyr) library(tidyr) library(tibble) feature_dir <- "data/features" metadata_dir <- "data/metadata" diversity_dir <- "data/diversity" report_dir <- "data/reports" dir.create(diversity_dir, recursive = TRUE, showWarnings = FALSE) dir.create(report_dir, recursive = TRUE, showWarnings = FALSE) feature_table_file <- file.path(feature_dir, "feature-table.tsv") sample_metadata_file <- file.path(metadata_dir, "sample-metadata.tsv") if (!file.exists(feature_table_file)) { stop( "Missing feature table: ", feature_table_file, "\nRun: bash scripts/bash/06a-create-example-feature-table.sh" ) } if (!file.exists(sample_metadata_file)) { stop( "Missing sample metadata: ", sample_metadata_file, "\nRun: bash scripts/bash/06a-create-example-feature-table.sh" ) } feature_table <- read_tsv(feature_table_file, show_col_types = FALSE) sample_metadata <- read_tsv(sample_metadata_file, show_col_types = FALSE) feature_matrix <- feature_table %>% column_to_rownames("feature_id") %>% as.matrix() sample_matrix <- t(feature_matrix) shannon_index <- function(x) { total <- sum(x) if (total == 0) { return(0) } p <- x[x > 0] / total -sum(p * log(p)) } alpha_diversity <- tibble( sample_id = rownames(sample_matrix), total_reads = rowSums(sample_matrix), observed_features = rowSums(sample_matrix > 0), shannon_diversity = apply(sample_matrix, 1, shannon_index) ) %>% left_join(sample_metadata, by = "sample_id") write_tsv( alpha_diversity, file.path(diversity_dir, "alpha-diversity.tsv") ) bray_curtis <- function(x, y) { numerator <- sum(abs(x - y)) denominator <- sum(x + y) if (denominator == 0) { return(0) } numerator / denominator } sample_ids <- rownames(sample_matrix) bray_matrix <- matrix( 0, nrow = length(sample_ids), ncol = length(sample_ids), dimnames = list(sample_ids, sample_ids) ) for (i in seq_along(sample_ids)) { for (j in seq_along(sample_ids)) { bray_matrix[i, j] <- bray_curtis(sample_matrix[i, ], sample_matrix[j, ]) } } bray_table <- as.data.frame(bray_matrix) %>% rownames_to_column("sample_id") write_tsv( bray_table, file.path(diversity_dir, "bray-curtis-distance-matrix.tsv") ) pcoa <- cmdscale(as.dist(bray_matrix), k = 2, eig = TRUE) ordination <- tibble( sample_id = rownames(pcoa$points), axis1 = pcoa$points[, 1], axis2 = pcoa$points[, 2] ) %>% left_join(sample_metadata, by = "sample_id") write_tsv( ordination, file.path(diversity_dir, "bray-curtis-pcoa.tsv") ) report <- tibble( metric = c( "sample_count", "feature_count", "alpha_metrics", "beta_metric", "ordination_method", "diversity_status" ), value = c( nrow(sample_matrix), ncol(sample_matrix), "observed_features; shannon_diversity", "Bray-Curtis", "PCoA using cmdscale", "READY_FOR_PLOTTING" ) ) write_tsv( report, file.path(report_dir, "diversity-analysis-report.tsv") ) message("Created:") message(" ", file.path(diversity_dir, "alpha-diversity.tsv")) message(" ", file.path(diversity_dir, "bray-curtis-distance-matrix.tsv")) message(" ", file.path(diversity_dir, "bray-curtis-pcoa.tsv")) message(" ", file.path(report_dir, "diversity-analysis-report.tsv")) ``` Run it from the MAS project root: ```bash Rscript scripts/R/08a-calculate-diversity-metrics.R ``` This creates: ```text data/diversity/alpha-diversity.tsv data/diversity/bray-curtis-distance-matrix.tsv data/diversity/bray-curtis-pcoa.tsv data/reports/diversity-analysis-report.tsv ``` ## 08b: Plot Diversity Results Save this script as: ```text scripts/R/08b-plot-diversity-results.R ``` ```r ############################################################################### # Microbiome Analysis System # 08b-plot-diversity-results.R # # Purpose: # Plot simple alpha diversity and beta diversity results. # # Usage: # Rscript scripts/R/08b-plot-diversity-results.R ############################################################################### library(readr) library(dplyr) library(ggplot2) diversity_dir <- "data/diversity" figure_dir <- "figures" table_dir <- "tables" dir.create(figure_dir, recursive = TRUE, showWarnings = FALSE) dir.create(table_dir, recursive = TRUE, showWarnings = FALSE) alpha_file <- file.path(diversity_dir, "alpha-diversity.tsv") ordination_file <- file.path(diversity_dir, "bray-curtis-pcoa.tsv") if (!file.exists(alpha_file)) { stop( "Missing alpha diversity file: ", alpha_file, "\nRun: Rscript scripts/R/08a-calculate-diversity-metrics.R" ) } if (!file.exists(ordination_file)) { stop( "Missing ordination file: ", ordination_file, "\nRun: Rscript scripts/R/08a-calculate-diversity-metrics.R" ) } alpha <- read_tsv(alpha_file, show_col_types = FALSE) ordination <- read_tsv(ordination_file, show_col_types = FALSE) write_tsv( alpha, file.path(table_dir, "alpha-diversity-for-plot.tsv") ) write_tsv( ordination, file.path(table_dir, "bray-curtis-pcoa-for-plot.tsv") ) p_alpha <- ggplot( alpha, aes( x = sample_id, y = shannon_diversity ) ) + geom_col() + labs( title = "Example Shannon Diversity", subtitle = "Toy MAS example data for workflow testing", x = "Sample", y = "Shannon diversity" ) + theme_minimal(base_size = 12) + theme( axis.text.x = element_text(angle = 45, hjust = 1) ) ggsave( filename = file.path(figure_dir, "alpha-shannon-diversity.png"), plot = p_alpha, width = 7, height = 5, dpi = 300 ) p_beta <- ggplot( ordination, aes( x = axis1, y = axis2, label = sample_id ) ) + geom_point(size = 3) + geom_text(vjust = -0.8) + labs( title = "Example Bray-Curtis PCoA", subtitle = "Toy MAS example data for workflow testing", x = "PCoA axis 1", y = "PCoA axis 2" ) + theme_minimal(base_size = 12) ggsave( filename = file.path(figure_dir, "bray-curtis-pcoa.png"), plot = p_beta, width = 7, height = 5, dpi = 300 ) message("Created:") message(" ", file.path(table_dir, "alpha-diversity-for-plot.tsv")) message(" ", file.path(table_dir, "bray-curtis-pcoa-for-plot.tsv")) message(" ", file.path(figure_dir, "alpha-shannon-diversity.png")) message(" ", file.path(figure_dir, "bray-curtis-pcoa.png")) ``` Run it from the MAS project root: ```bash Rscript scripts/R/08b-plot-diversity-results.R ``` This creates: ```text tables/alpha-diversity-for-plot.tsv tables/bray-curtis-pcoa-for-plot.tsv figures/alpha-shannon-diversity.png figures/bray-curtis-pcoa.png ``` ## Running the Complete Diversity Example If you are continuing from Chapter 06 or 07, first make sure the example feature table exists: ```bash bash scripts/bash/06a-create-example-feature-table.sh bash scripts/bash/06b-check-feature-table.sh ``` Then calculate and plot diversity results: ```bash Rscript scripts/R/08a-calculate-diversity-metrics.R Rscript scripts/R/08b-plot-diversity-results.R cat data/reports/diversity-analysis-report.tsv cat data/diversity/alpha-diversity.tsv ``` The toy example contains only three samples and five features. This is enough to test workflow structure, but it is not enough for real statistical interpretation. ## Example Alpha Diversity Output The generated alpha diversity table may look like this: ```text sample_id total_reads observed_features shannon_diversity group sample_type description SRR17868090 185 4 0.978 healthy stool toy example sample 1 SRR17868091 169 4 1.253 healthy stool toy example sample 2 SRR17868092 186 5 1.417 healthy stool toy example sample 3 ``` Values will depend on the feature table used. ## Example Beta Diversity Output The Bray-Curtis distance matrix summarizes pairwise community dissimilarity. ```text sample_id SRR17868090 SRR17868091 SRR17868092 SRR17868090 0.000 0.231 0.542 SRR17868091 0.231 0.000 0.328 SRR17868092 0.542 0.328 0.000 ``` Lower values indicate more similar samples. Higher values indicate more different samples. ## Interpreting Diversity Results Diversity results should be interpreted in relation to: - sample type - sequencing depth - feature generation method - filtering decisions - biological group - metadata completeness - batch effects - study design - sample size A diversity difference may be biologically interesting, but it should not be interpreted in isolation. ## Common Diversity Analysis Issues Common issues include: - comparing samples with very different sequencing depths - interpreting ordination separation without statistical support - ignoring batch effects - using metrics that do not match the biological question - overinterpreting small sample sizes - mixing datasets generated by different methods - failing to document filtering and normalization choices These issues should be considered before reporting diversity findings. ## MAS Diversity Outputs At the end of this stage, MAS should have: - alpha diversity table - beta diversity distance matrix - ordination coordinates - diversity analysis report - alpha diversity plot - beta diversity plot - notes on diversity metrics and limitations ```{mermaid} flowchart LR A[Feature Table] --> B[Alpha Diversity] A --> C[Beta Diversity] B --> D[Diversity Report] C --> D D --> E[Biological Interpretation] ``` ## Key Takeaways Diversity analysis summarizes microbial community structure. A strong diversity analysis stage ensures that: - alpha diversity is calculated and interpreted carefully - beta diversity is linked to sample metadata - ordination plots are not overinterpreted - sequencing depth and feature filtering are considered - diversity metrics match the biological question - limitations are documented Diversity analysis provides important evidence, but it is one part of the broader microbiome interpretation system. ## What Comes Next The next chapter examines **Functional Profiling**, where microbiome data are used to explore gene families, pathways, and microbial functional potential.