PyWGCNA is a Python package designed to do Weighted Gene Correlation Network analysis (WGCNA)
-
Updated
Jul 24, 2024 - Jupyter Notebook
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)
An R package for weighted region comethylation network analysis
Construction of Gene Expression Network across 3 brain regions in the presence of Ethanol using WGCNA in R
Investigate the role of mtDNA in the sex determination/development of Potamilus streckersoni, a freshwater mussel with doubly uniparental mitochondrial inheritance. Scripts for DESeq2, WGCNA, GSEA, AlphaFold/AlphaPulldown, and mt-sncRNA validation.
Algorithms for NCBI, SRA, EBI datasets recommendation and how to get around with comparing your own Datasets. Recommender systems | Bio-NLP
GWAS of Postmortem Brain Samples Sheds Light on the Development of Schizophrenia and Bipolar Disorder
Weighted Gene Co-expression Network Analysis;
Code for the (unpublished) paper ( Regulation of autophagy in response to oxidative stress)
Code for Walker, Saunders, Rai et al., (2021).
Differential Expression Analysis of protein, Gene set enrichment analysis, Multi-omic factor analysis, Pathway analysis, WGCNA
Experimental code for analysis of miRNA expression in C2C12 vs. in vivo mice cells
The Summer_WGCNA_Discussion repository contains scripts that can perform weighted gene correlation network analysis (WGCNA).
Online app for WGCNA Analysis
Add a description, image, and links to the wgcna topic page so that developers can more easily learn about it.
To associate your repository with the wgcna topic, visit your repo's landing page and select "manage topics."