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This repository contains all the code to implement all examples showed in our paper:

A network perspective on the ecology of gut microbiota and progression of Type 2 Diabetes: linkages to keystone taxa in a Mexican cohort

Data

We obtained the data from a mexican cohort of Type 2 Diabetes (T2D) patients from Diener et al 2021. Abundances and taxonomy of the samples can be found here.

We organized the data in separated tables according to their T2D status, healthy, impaired fasting glycemia (IFG), impaired glucose tolerance (IGT), IFG + IGT, DT2 and Diabetes Type 2 treated (DT2_treated), you can find this files in the folder data.

Requirements

  • SparCC
  • Python = 2.7
    • Numpy = 1.16.5
    • Pandas = 0.22.0
  • R packages
    • tidyverse
    • visNetwork
    • EdgeR
    • Phyloseq

Methodology

The next figure presents the methods used in our paper. Every step is detailled in the following sections

methods_flowchart

Network inference

Correlations

We used SparCC to infer microbial interactions for every group of study. Run the next command in your terminal to infer the microbial network:

bash net_inference_sparcc.sh

Network preparation

Run the next script in R to get the nodes and edges of each network for every group of study:

net_preparation.R

Note: you will need to run an additional script in python. It is available in notebook format.

Network visualization

Run the next script in R, it will create an html file with the network visualization:

net_visualization.R

Network analysis

In this step we used Netshift to evaluate the topoligical features of each network and also to compare between different clinical conditions. To do that upload the filtered tables previously obtained during the network preparation step. Netshift has an online interface, you can find it here

We perfomed the following comparisons between groups.

Control Case
Healthy IFG
IFG IGT
IGT IFG + IGT
IFG + IGT T2D
T2D T2D_treated

Differential abundance analysis

In this step, we used EdgeR to infer how the abundance of gut microbiota is related to the clinical status of patients. The analysis was carried out at the genus level using the same groups described above.

Microbial structure analysis

We used Upset R library to generate absence/presence plots from the nodes network for each clinical condition.

Results

Please, follow the next links to visualize the results

Krona Plots

Networks

The networks follow the next shape code for each group population:

State Shape
Healthy Square
IFG Triangle
IGT Diamond
IFG + IGT Star
T2D Dot
T2D_Treated Triangle down

The color of each node refers to a specific Phylum and they are indicated in the networks graph.