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IMAP: Integrated Microbiome Analysis Pipelines: End-to-End Practical User Guides Using Integrated Approaches

11 What’s Next: Utilizing Transformed Data

After computing distances using various methods, such as Bray-Curtis, Jaccard, Euclidean, and Aitchison, we can harness these transformed data in downstream analyses. These analyses may encompass:

  • Beta Diversity Analysis: Comparing microbial community compositions between samples to elucidate differences or similarities.
  • Multivariate Statistical Analysis: Assessing the significance of differences between groups of samples using methods like PERMANOVA or MANOVA.
  • Data Visualization: Identifying patterns or clusters within the microbial community data through visualization techniques such as PCoA or NMDS plots.
  • Correlation Analysis: Exploring relationships between microbial taxa and environmental variables to uncover potential ecological associations.
  • Dimensionality Reduction: Employing techniques like Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), or Uniform Manifold Approximation and Projection (UMAP) to visualize high-dimensional microbiome data in lower-dimensional spaces.

By leveraging the transformed data from distance calculations, we can gain deeper insights into microbial community dynamics and their relationships with various environmental factors or experimental conditions.