🦠 A comprehensive R package for deep mining microbiome

Network construction, analysis, and comparison for microbial compositional data

A list of R environment based tools for microbiome data exploration, statistical analysis and visualization

This repo contains the R microshades package, which contains a color blind accessible color palette with 30 unique colors and functions for applying these colors to microbiome data.

Track, Analyze, Visualize: Unravel Your Microbiome's Temporal Pattern with MicrobiomeStat

Analyses in R and Python Using curatedMetagenomicData

GDmicro - Use GCN and Deep adaptation network to classify host disease status based on human gut microbiome data.

A Bayesian model for the differential abundance analysis of microbiome sequencing data

The scripts contained in this repository relate directly to the work conducted by the Tree Root Microbiome Project (TRMP) led by Dr Steve Wakelin.

Microbiome Diversity Inspector - A platform for visual analysis of microbiome data.

Code to reproduce the analyses from the paper "Benchmarking microbiome transformations favors experimental quantitative approaches to address compositionality and sampling depth biases"

A database for storing and analyzing omics data

A Shiny interface for the MicrobiomeStat R package, designed to facilitate analysis and visualization of microbiome data.

MicrobiomeStat Tutorial Repository: This is a comprehensive resource for learning how to use the MicrobiomeStat package. It provides a step-by-step guide to effectively analyze complex microbiome data.

Analyzing microbiome data from Gigante, Barro Colorado, Panama. Part of an Undergraduate Thesis (defended on May 13th, 2022).

A microbiome knowledge graph was constructed using data from the Human Microbiome Project, IBD cohort. This project was done as a part of my dissertation for my MSc in Health Data Science with The University of Manchester. It was done as an internship with Zifo RnD Solutions, a leading Scientific Informatics Company.

Python utility to generate distance matrices, perform PCoAs and generate taxonomic summaries using simple tab-separated feature tables.

I performed PCA and t-SNE on the microbiome dataset and visualized the data in 2D space. I also report what I learned from the PCA and t-SNE analyses.

This is the data and R code associated with our paper "A t-SNE Based Classification Approach to Compositional Microbiome Data"