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Leo Lahti edited this page Jul 11, 2025 · 75 revisions

Roadmap

Contents to add

  • (Probabilistic?) mixed effects model examples in the DAA section?
  • Catboost

Enrichment analyses

Comparison points

Additional functionality

Sample size

Sample size calculator for microbiome data would be helpful for many.

  • mPower by Lu Yang is now in the making

  • Ref 1

  • Ref 2

  • Multiple testing post-hoc correction for PERMANOVA, examples (CR): mctoolsr::calc_pairwise_permanovas

Distances and clustering

GUniFrac: Generalized UniFrac Distances, Distance-Based Multivariate Methods and Feature-Based Univariate Methods for Microbiome Data Analysis CRAN package with A suite of methods for powerful and robust microbiome data analysis including data normalization, data simulation, community-level association testing and differential abundance analysis. It implements generalized UniFrac distances, Geometric Mean of Pairwise Ratios (GMPR) normalization, semiparametric data simulator, distance-based statistical methods, and feature-based statistical methods. The distance-based statistical methods include three extensions of PERMANOVA: (1) PERMANOVA using the Freedman-Lane permutation scheme, (2) PERMANOVA omnibus test using multiple matrices, and (3) analytical approach to approximating PERMANOVA p-value. Feature-based statistical methods include linear model-based methods for differential abundance analysis of zero-inflated high-dimensional compositional data.

POSTm: Phylogeny-Guided OTU-Specific Association Test for Microbiome Data - Implements the Phylogeny-Guided Microbiome OTU-Specific Association Test method, which boosts the testing power by adaptively borrowing information from phylogenetically close OTUs (operational taxonomic units) of the target OTU. This method is built on a kernel machine regression framework and allows for flexible modeling of complex microbiome effects, adjustments for covariates, and can accommodate both continuous and binary outcomes.

Embeddings

GMEmbeddings: An R Package to Apply Embedding Techniques to Microbiome Data

Probabilistic workflows

  • alto nested topic models
  • fido (stray) for Bayesian analysis of balances
  • Probabilistic topic models / LDA and differential topic analysis from Holmes & Jeganathan
  • Probabilistic PCA implementation and examples for miaverse
  • Multiple testing correction vs. hierarchical probabilistic testing
  • Power calculations

Differential abundance

  • LinDA
  • songbird
  • More examples on ratio-based tests
  • ANCOM-II not mentioned in OMA examples (ANCOM-BC is but it is not in this review; which one is better..?)

Balances

Tasks

Phylogenetic tree support

  • OGU support
  • phylofactor; support for phylogenetic factorization based on phILR
  • Using tidytree? Could be processed and visualized via tidytree, treeio, ggtree and ggtreeExtra

Heatmaps

Add a link to iheatmapr; consider if an example would be useful (or not) in addition to pheatmap examples.

Generic points on heatmaps:

  • meltAssay is readily useful to prepare data for ggplot-based heatmaps; is there a need for other tse data converters for pheatmap or some other heatmap packages?

  • Check if there are good existing resources for heatmaps on the single cell experiment side (OSCA book?) or other

  • Consider heatmap example with philr balances, in addition to typical abundance-based heatmaps. First finalize the philr PR, however.

  1. Add neat & neatsort in miaViz and then also add heatmap examples to OMA using these?

Data resources

  • qiitr; add support for data retrieval and analysis from QIITA

Multi-assay association & integration

Sankaran & Holmes (2019) Multitable Methods for Microbiome Data Integration

POMS for Integrating phylogenetic and functional data in microbiome studies. Available as an R package

Prospective analysis

Interactivity

  • iSEE

  • iSEEde for DA analysis

  • Interactive tables

  • 3D visualizations

  • Interactive heatmap example with heatmaply after we have this on a server?

Review

Time series

BiomeHorizon: Visualizing Microbiome Time Series Data in R biomehorizon pkg biomehorizon user guide

Turnover indices

Visualization

Additional documentation

  • Walkthrough to TreeSE from microbiome perspective? First review resources in other sources, including SE, TreeSE, SCE, OSCA, scater, seurat etc.
  • Rank abundance curve
  • Forest plot
  • UpSet plots
  • Power calculations
  • RDA & other supervised ordination techniques
  • Table examples
  • Density plot
  • Volcano plot
  • Horseshoe effects
  • Visualization artefacts (enterotype w/o colors; forced clustering; biased groups etc.) for educational purposes
  • Cross-sectional
  • Case-control
  • Intervention
  • Individual
  • Longitudinal time series analysis & simulation (seqtime/microsimR integration)?
  • Spatial
  • Networks
  • Tipping elements & bimodality
  • Forensics / source tracking ref1; ref2

Request support to external packages

  • file2meco
  • maaslin2 (Mallick H et al., 2020)
  • specificity

Manuscript

Write the manuscript collaboratively using Manubot

Training material and additional content

Containers

  • Add comparison between TreeSE vs. phyloseq systematically; what are the main differences, what are key similarities, what are the pros & cons of each, how to convert? MAE to bind.

  • Speed comparisons?

Beta diversity

Explain Difference between PLS-DA, (dbRDA), ... good short explanation and considering adding support for PLS-DA (from mixOmics for instance)

Power calculations

Tree-based methods

  • Examples on tree based alpha, beta, diffab (hierarchical tests?)

Summary slides

  • microbiome workflow
  • analysis types

Packages with useful tools

Go through for useful functionality (supporting SE/SCE):

Alpha indices

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