Orchestrating Microbiome Analysis with Bioconductor

Tuomas Borman

Orchestrating Microbiome Analysis with Bioconductor

• Data containers
• TreeSummarizedExperiment class
• mia ecosystem

Data containers

• Data containers form the core of software
• Modular, efficient workflows

SummarizedExperiment

(Huber et al. 2015)

Microbiome data science

• Sequencing data - Other omics regularly incorporated
• Previously based on phyloseq

(Moreno-Indias et al. 2021)

Optimal container for microbiome data?

Optimal container for microbiome data?

• Multiple assays: taxonomic and functional profiles

Optimal container for microbiome data?

• Multiple assays: taxonomic and functional profiles
• Hierarchical data: phylogenetic tree

Optimal container for microbiome data?

• Multiple assays: taxonomic and functional profiles
• Hierarchical data: phylogenetic tree
• Side information: sample and feature metadata

Optimal container for microbiome data?

• Multiple assays: taxonomic and functional profiles
• Hierarchical data: phylogenetic tree
• Side information: sample and feature metadata
• Optimized: for speed and memory

Optimal container for microbiome data?

• Multiple assays: taxonomic and functional profiles
• Hierarchical data: phylogenetic tree
• Side information: sample and feature metadata
• Optimized: for speed and memory
• Interoperable: with other applications and frameworks

Optimal container for microbiome data?

• Multiple assays: taxonomic and functional profiles
• Hierarchical data: phylogenetic tree
• Side information: sample and feature metadata
• Optimized: for speed and memory
• Interoperable: with other applications and frameworks

Reduce overlapping efforts, improve interoperability, ensure sustainability.

TreeSummarizedExperiment

(Huang et al. 2021)

Microbiome Analysis (mia)

• Microbiome data science in SummarizedExperiment ecosystem
• Distributed through several R packages
• mia package top 8.1% Bioconductor downloads

Community-driven ecosystem of tools

• mia (Data analysis)
• miaViz (Visualization)
• miaSim (Simulation)
• miaTime (Time series analysis)
• miaDash (Graphical user interface)
• iSEETree (Interactive visualization)
• Expanded by independent developers

Community-driven ecosystem of tools

• mia (Data analysis)
• miaViz (Visualization)
• miaSim (Simulation)
• miaTime (Time series analysis)
• miaDash (Graphical user interface)
• iSEETree (Interactive visualization)
• Expanded by independent developers

Interoperable with the SummarizedExperiment ecosystem

Online book

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

Acknowledgements

• Giulio Benedetti
• Muluh Geraldson
• Aura Raulo
• Akewak Jeba
• Felix M. Ernst
• Sudarshan Shetty
• Christian L. Müller
• Aki Havulinna
• Levi Waldron
• Thomaz Bastiaanssen
• Leo Lahti
• and others

Thank you for your time!

Moreno-Indias et al. (2021) Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions. Frontiers in Microbiology.

References

Huang, Ruizhu, Charlotte Soneson, Felix G. M. Ernst, et al. 2021. “TreeSummarizedExperiment: A S4 Class for Data with Hierarchical Structure.” F1000Research 9: 1246. https://doi.org/10.12688/f1000research.26669.2.

Huber, W., V. J. Carey, R. Gentleman, S. Anders, M. Carlson, B. S. Carvalho, H. C. Bravo, et al. 2015. “Orchestrating High-Throughput Genomic Analysis with Bioconductor.” Nature Methods 12 (2): 115–21. http://www.nature.com/nmeth/journal/v12/n2/full/nmeth.3252.html.

Moreno-Indias, Isabel, Leo Lahti, Miroslava Nedyalkova, Ilze Elbere, Gennady V. Roshchupkin, Muhamed Adilovic, Onder Aydemir, et al. 2021. “Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions.” Frontiers in Microbiology 12: 277. https://doi.org/10.3389/fmicb.2021.635781.