Bioconductor

• Community-driven open-source project

1. Training programs & workshops
2. Conferences & community support
3. Bioinformatics software

Bioconductor

• Community-driven open-source project

1. Training programs & workshops
2. Conferences & community support
3. Bioinformatics software

Software

• ~2,300 R packages
• Review, testing, documentation

TreeSummarizedExperiment

MIcrobiome Analysis (mia)

• Microbiome data science ecosystem
• Distributed through several R packages
• mia package top 4.4% Bioconductor downloads

Advantages

• Shared data container
• Scalable & optimized for large datasets
• Comprehensive documentation

Allows us to develop efficient microbiome data science workflows

Orchestrating Microbiome Analysis with Bioconductor

• Resources and tutorials for microbiome analysis
• Community-built best practices
• Open to contributions!

Reproducible reporting

• To create human-readable reports
• Transparency, reusability and reproducibility
• Debugging

Literate programming

Programming paradigm introduced by Donald Knuth (1984) in which a computer program is given as an explanation of its logic in a natural language, embedded with code chunks, from which compilable source code can be generated.
(Adapted from Wikipedia)

Reproducible notebooks

• We use Quarto for reproducible documentation.
• Next generation development of Rmarkdown
• Supported by RStudio

Demonstration

Exercises

From OMA online book, Chapter 1: Microbiome data science in Bioconductor

microbiome.github.io/OMA/docs/devel/pages/intro.html

• Exercise 2
• Exercise 3
• Exercise 4

Demonstration

Exercises

From OMA online book, Chapter 3: Data containers

• All exercises

From OMA online book, Chapter 10: Subsetting

• All exercises

Agglomeration

• Interested only higher level of taxonomy ranks
• Reduce noise

–> Summarize rows to higher taxonomy rank

Demonstration

Exercises

From OMA online book, Chapter 11: Agglomeration

• 1.2, 1.3, 1.4, 1.5, 1.6

Microbiome data

High variability

• Abundance of feature varies a lot from sample to sample

Zero-inflation

• Many taxa absent in most samples

Compositionality

• Only relative abundances observed
• Total counts per sample are arbitrary

Compositionality

Transformation

• Mitigate biases
• Make comparable
• Meet assumptions of statistical test

Demonstration

Alpha diversity

• Diversity within a sample
• Richness vs evenness

Demonstration

Exercises

From OMA online book, Chapter 12: Transformation

• 1.2, 1.3, 1.4, 1.5, 1.6

From OMA online book, Chapter 14: Alpha diversity

• 1.2, 1.3, 1.4, 1.9, 1.10

From OMA online book, Chapter 13: Community composition

• 1.2, 1.3, 1.4, 1.5

Beta diversity

• Similarity between samples
• Dissimilarity/distance between samples, clustering, ordination…

Ordination methods

• PCA, PCoA/MDS, RDA, …
• Euclidean vs non-Euclidean
• Unsupervised vs supervised

Principal component analysis (PCA)

• Unsupervised ordination method
• Euclidean distance
• Aitchison distance: CLR + Euclidean distance

Principal coordinate analysis (PCoA)

• Multidimensional scaling (MDS)
• Unsupervised ordination method
• Any dissimilarity metric (e.g., Bray-Curtis dissimilarity)

Redundancy analysis (RDA)

• Supervised ordination method
• Find variance explained by sample metadata

Demonstration

Exercises

From OMA online book, Chapter 15: Community similarity

• Exercise 1

Exercises

From OMA online book, Chapter 15: Community similarity

• Exercise 1
• Exercise 2

Exercises

From OMA online book, Chapter 15: Community similarity

• Exercise 1
• Exercise 2
• Exercise 3

Differential abundance analysis (DAA)

• Identify taxa whose abundance differs between groups
• Classical statistical tests
• Methods dedicated for microbiome data

Elementary methods provide more replicable results in microbial differential abundance analysis

• Relative abundances with a Wilcoxon test
• Log-transformed relative abundances with a t-test
• Presence/absence of taxa with logistic regression

Pelto et al. 2025

Relative abundance

\[ \text{X}_{ij} = \frac{\text{Count}_{ij}}{\sum_{k=1}^m \text{Count}_{ik}} \]

where

• \(i\) indexes the sample,
• \(j\) indexes the taxon,
• \(m\) is the total number of taxa.

Log-transformed relative abundance

\[ \text{X}_{ij} = \log \left( \text{Relative abundance}_{ij} + \epsilon \right) \]

where

• \(\epsilon\) is a small pseudocount to avoid \(\log(0)\).

Wilcoxon vs t-test

Elementary methods provide more replicable results in microbial differential abundance analysis

We are developing a package containing all these methods

Exercises

From OMA online book, Chapter 17: Differential abundance

• All exercises

Multiomics integration

• Data containers
• Methods

Alternative experiment

• altExp()
• Slot in TreeSummarizedExperiment
• One-to-one sample mapping

Methods

• Association
• Ordination
• Supervised machine learning

Cross-association

• CLR + Spearman

Centered log-ratio (CLR)

\[ \text{CLR}(x_i) = \log\left(\frac{x_i}{g(x)}\right),\quad g(x) = \left(\prod_{j=1}^{D} x_j \right)^{1/D} \]

• ( x_i ): the i-th component of the composition
  
• ( g(x) ): geometric mean of the composition

\[ \text{Arithmetic mean} = \frac{2 + 8 + 32}{3} = \frac{42}{3} = 14 \]

\[ \text{Geometric mean} = \sqrt[3]{2 \cdot 8 \cdot 32} = \sqrt[3]{512} = 8 \]

• Removes compositional constraints (e.g., constant sum)
• Allows use of standard statistical tools (e.g. PCA)
• Symmetric: values centered around zero

Pearson correlation

• “Normalized covariance”
• Spearman rho = Pearson calculated for ranks of values

\[ r = \frac{ \sum_{i=1}^{n} (x_i - \bar{x})(y_i - \bar{y}) }{ \sqrt{ \sum_{i=1}^{n} (x_i - \bar{x})^2 } \cdot \sqrt{ \sum_{i=1}^{n} (y_i - \bar{y})^2 } } \]

Demonstration

Exercises

From OMA online book, Chapter 23: Cross-association

• All exercises