Welcome to MEGMA tutorial Pages!

This tutorial is about the metagenomic deep learning and biomarker discovery based on MEGMA.

MEGMA

MEGMA is short for metagenomic Microbial Embedding, Grouping, and Mapping Algorithm (MEGMA) , which is a further step development of AggMap that specific for metagenomic data learning. MEGMA is developed to transform the tabular metagenomic data into spatially-correlated color image-like 2D-representations, named as the 2D-microbiomeprints (3D tensor data in the form of row, column and channel). 2D-microbiomeprints are multichannel feature maps (Fmaps) and are the inputs of ConvNet-based AggMapNet models.

MEGMA is released in the aggmap package, in this tutorial, we will show how to employ the aggmap package for MEGMA implementary.

for metagenomic-based disease prediction by deep learning model and identifying the important signatures.

Tutorial Content

Metagenomic deep learning and biomarker discovery

• 1. Introduction

  • 1.1 MEGMA introduction
  • 1.2 Metagenomic cross nation datasets and tasks
  • 1.3 MEGMA fitting and AggMapNet training strategy
• 2. MEGMA Training & Transformation
  • 2.1 Fitting MEGMA on metagenomic abundance data of all countries
    • 2.1.1 Read and preprocess data for MEGMA
    • 2.1.2 MEGMA initialization, fitting and dump
    • 2.1.3 MEGMA loading and 2D-microbiomeprints transformation
    • 2.1.4 MEGMA 2D-microbiomeprints visulization
    • 2.1.5 Well-trained MEGMA to transform the abundance data of each country
  • 2.2 Fitting MEGMA on metagenomic abundance data of one country only
    • 2.2.1 Read and preprocess data for MEGMA
    • 2.2.2 MEGMA initialization & fitting
    • 2.2.3 MEGMA 2D-microbiomeprints transformation
    • 2.2.4 MEGMA Fmaps visulization
    • 2.2.5 Transform the abandance data of the rest countries by country-specific MEGMA
    • 2.2.6 Fitting country-specific megma for all countries
  • 2.3 Discussions & conclusions on MEGMA 2D-microbiomeprints
• 3. Training the CRC detection models based on MEGMA Fmaps
  • 3.1 Training and test AggMapNet on overall MEGMA Fmaps
  • 3.2 Training and test AggMapNet on country specific MEGMA Fmaps
  • 3.3 Comparing the STST performance and discussion
• 4. Important microbial marker identification
  • 4.1 Calculate the global feature importance
    • 4.1.1 GFI for model trained on overall MEGMA Fmaps
    • 4.1.2 GFI for model trained on country specific MEGMA Fmaps
  • 4.2 Generate the explaination saliency map
    • 4.2.1 Saliency map for overall MEGMA Fmaps
    • 4.2.2 Saliency map country specific MEGMA Fmaps
  • 4.3 Global feature importance correlation
  • 4.4 Discussions and conclusions on saliency map
• 5. Toplogical analyisis on the important microbes
  • 5.1 Plotting the the embedded and arranged microbes
  • 5.2 Plotting the linear assignment of the embedded microbes
  • 5.3 Fetching the optimized toplogical graph and clustering
  • 5.4 Exploring the toplogical relationship of the important microbes
• 6. Exploring the embedding & grouping in MEGMA
  • 6.1 Microbial embedding
    • 6.1.1 Manifold embedding
    • 6.1.2 Ramdom embedding
  • 6.2 Microbial grouping
    • 6.2.1 Hierarchical clustering tree based grouping
    • 6.2.2 Taxonomic tree based grouping