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Statistical Methods for Microbiome Analysis


June 20 to 23. AFTERNOON: 15 to 19h (June 20, 21, and 22) and 15 to 18h (June 23)


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Malu Calle

M.Luz Calle is Full Professor of Biostatistics and Bioinformatics and Head of the Biosciences Department, University of Vic – Central University of Catalonia.

With a background in Mathematics (BSc Mathematics, Universitat de Barcelona, 1986 and PhD in Mathematics, Universitat Politècnica de Catalunya, 1997), she teaches biostatistics, bioinformatics and genetic epidemiology in the Biotechnology degree and is the chair of the Master of Sciences in Omics Data Analysis, where she teaches the course “Statistical and data-mining methods for omics data analysis”.

She is the group leader of the Bionformatics and Medical Statistics Group of the University of Vic (consolidated group 2017SGR-199).

Her main research areas are statistical genetics, omics data analysis, microbiome data analysis and survival analysis. She works on the development of new methods for biomarker discovery, identification of genetic risk profiles and construction of dynamic prediction and prognostic models of disease evolution. She is also interested in statistical methods for integration of multi-omics data and compositional data approaches in metagenomics.

She is member of several scientific societies: BiostatNet-Spanish National Network in Biostatistics, Catalan Statistical Society, Spanish Society of Statistics and Operational Research, International Biometric Society, International Genetic Epidemiology Society.

Former President of the Spanish Region of the International Biometrics Society (2012-2013) and Vicepresident (2014).





Understanding the role of the microbiome in human health and how it can be modulated is becoming increasingly relevant for preventive medicine and for the medical management of chronic diseases. High-throughput sequencing technologies has boosted microbiome research but the compositional nature of microbiome data is a major challenge for their analysis.

This course provides a self-contained introduction to microbiome data analysis. A brief introduction to concepts in microbial ecology and microbial DNA sequencing will be given. We will introduce the R package “coda4microbiome”, available at CRAN, that aims to bridge the gap between microbiome research and compositional data analysis (CoDA), for both, cross-sectional and longitudinal studies.

Course goals

  • Recognize the main goals and challenges of microbiome statistical analysis
  • Understand and apply standard statistical methods for microbiome analysis
  • Understand and apply innovative statistical approaches for microbiome analysis within the CoDA framework

Course contents

  1. The human microbiome
  2. NGS microbiome studies
    1. Microbial DNA extraction and sequencing
    2. 16S ribosomal RNA gene
    3. 16S Bioinformatics pipeline
  3. Microbiome statistical analysis
    1. Main goals and challenges of microbiome statistical analysis
    2. Exploratory analysis - Abundance plots
    3. Ecological measures of richness and diversity
    4. Ordination: Visualization of beta diversity
    5. Standard methods for microbiome differential abundance testing
  4. Compositional data analysis of microbiome cross-sectional studies
    1. Exploratory analysis of log-ratios
    2. Identification of microbial signatures
  5. Compositional data analysis of microbiome longitudinal studies
    1. Log-ratio analysis of microbiome longitudinal data
    2. Identification of dynamic microbial signatures


Some experience with R is required

Targeted at

Targeted at any person interested in the analysis of microbiome data


Assistance and assessment of practical exercises

Software requirements

Participants should bring their own laptops with RStudio installed.