Alban Ramette reviews multivariate analyses in microbial ecology, emphasizing their importance in interpreting complex data sets and identifying patterns related to environmental factors. As biological data and environmental parameters accumulate, microbial ecologists can better understand diversity patterns and their environmental drivers. Multivariate techniques, such as cluster analysis, principal component analysis (PCA), correspondence analysis, and redundancy analysis, are essential for exploring and explaining these patterns. These methods help reduce data complexity, identify major patterns, and uncover causal factors. The review discusses data types, preparation, and transformations necessary for multivariate analysis. It highlights the importance of standardization and normalization to ensure variables are comparable. Data transformations, such as the Hellinger and chord transformations, are recommended for handling community composition data with many zeros. Exploratory analyses, including cluster analysis and PCA, are used to visualize and explore complex data sets. Cluster analysis groups objects based on similarities, while PCA reduces data dimensions by creating new variables that capture the most variance. These methods are useful for identifying patterns but do not directly explain their causes. Correspondence analysis (CA) is used to examine relationships between species and environmental gradients, particularly when species exhibit unimodal responses. CA is effective for ecological niche analysis and can reveal patterns in microbial communities. However, it may produce an "arch" effect, which can be corrected using detrended correspondence analysis (DCA). Nonmetric multidimensional scaling (NMDS) is another technique for visualizing data based on ranked distances, preserving the order of objects rather than their actual distances. It is useful for identifying underlying gradients and comparing community structures. Statistical tests, such as nonparametric multivariate ANOVA (NPMANOVA) and analysis of similarities (ANOSIM), are used to assess significant differences between groups. These tests are important for determining whether observed patterns are statistically significant. Environmental interpretation involves linking observed patterns to environmental variables. Techniques like ANOSIM help determine if microbial communities differ significantly between groups, such as spatial or temporal differences. These methods are crucial for understanding how environmental factors influence microbial assemblages. The review emphasizes the need for careful application of multivariate techniques to avoid misinterpretation and to ensure accurate ecological insights.Alban Ramette reviews multivariate analyses in microbial ecology, emphasizing their importance in interpreting complex data sets and identifying patterns related to environmental factors. As biological data and environmental parameters accumulate, microbial ecologists can better understand diversity patterns and their environmental drivers. Multivariate techniques, such as cluster analysis, principal component analysis (PCA), correspondence analysis, and redundancy analysis, are essential for exploring and explaining these patterns. These methods help reduce data complexity, identify major patterns, and uncover causal factors. The review discusses data types, preparation, and transformations necessary for multivariate analysis. It highlights the importance of standardization and normalization to ensure variables are comparable. Data transformations, such as the Hellinger and chord transformations, are recommended for handling community composition data with many zeros. Exploratory analyses, including cluster analysis and PCA, are used to visualize and explore complex data sets. Cluster analysis groups objects based on similarities, while PCA reduces data dimensions by creating new variables that capture the most variance. These methods are useful for identifying patterns but do not directly explain their causes. Correspondence analysis (CA) is used to examine relationships between species and environmental gradients, particularly when species exhibit unimodal responses. CA is effective for ecological niche analysis and can reveal patterns in microbial communities. However, it may produce an "arch" effect, which can be corrected using detrended correspondence analysis (DCA). Nonmetric multidimensional scaling (NMDS) is another technique for visualizing data based on ranked distances, preserving the order of objects rather than their actual distances. It is useful for identifying underlying gradients and comparing community structures. Statistical tests, such as nonparametric multivariate ANOVA (NPMANOVA) and analysis of similarities (ANOSIM), are used to assess significant differences between groups. These tests are important for determining whether observed patterns are statistically significant. Environmental interpretation involves linking observed patterns to environmental variables. Techniques like ANOSIM help determine if microbial communities differ significantly between groups, such as spatial or temporal differences. These methods are crucial for understanding how environmental factors influence microbial assemblages. The review emphasizes the need for careful application of multivariate techniques to avoid misinterpretation and to ensure accurate ecological insights.