PyWGCNA is a Python package designed to do Weighted Gene Correlation Network analysis (WGCNA)

Hierarchical, iterative clustering for analysis of transcriptomics data in R

A step-by-step tutorial for Weighted correlation network analysis (WGCNA)

shiny app for WGCNA...

Uncovering Amiloride's Mechanisms of Action in Multiple Myeloma: A Bioinformatics Approach

An R package for weighted region comethylation network analysis

This repository contains a Script for WGCNA based on the Tutorial of WGCNA page

Differential Expression Analysis of protein, Gene set enrichment analysis, Multi-omic factor analysis, Pathway analysis, WGCNA

Repository for all R scripts and results associated with my research dissertation for the MSc Bioinformatics and Computational Biology degree in University College Cork, 2022.

attempt a non-bash pipeline in nextflow

GWAS of Postmortem Brain Samples Sheds Light on the Development of Schizophrenia and Bipolar Disorder

Online app for WGCNA Analysis

Code for Walker, Saunders, Rai et al., (2021).

Algorithms for NCBI, SRA, EBI datasets recommendation and how to get around with comparing your own Datasets. Recommender systems | Bio-NLP

Multivariate Analysis Using Co-Expression Network Modeling Identifies Specific Inflammation and Diffusion MRI Features in Major Depressive Disorder

Construction of Gene Expression Network across 3 brain regions in the presence of Ethanol using WGCNA in R

Weighted Gene Co-expression Network Analysis;

Self-contained R and Python modules for common bioinformatics analyses. Each runs on a bundled example dataset and produces vector, journal-style figures: transcriptomics, single-cell, machine learning, Mendelian randomization, WGCNA, and more.

Gene expression and network analysis for multiple RNA-seq datasets associated with heat-stress in abalone