WGCNA Script for: Monocyte, Neutrophil and Whole Blood Transcriptome Dynamics Following Ischemic Stroke

07/28/2022

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Monocyte, Neutrophil and Whole Blood Transcriptome Dynamics Following Ischemic Stroke
Paulina Carmona-Mora, Bodie Knepp, Glen C Jickling, Xinhua Zhan, Marisa Hakoupian, Heather Hull, Noor Alomar, Hajar Amini, Frank R Sharp, Boryana Stamova, Bradley P Ander
BMC Med 21, 65 (2023). https://doi.org/10.1186/s12916-023-02766-1

Overview

This script was used to generate the Monocyte (MON), Neutrophil (NEU), and Whole Blood (WB) Weighted Gene Co-Expression Network Analyses (WGCNA) Networks for Carmona-Mora et al’s “Monocyte, Neutrophil and Whole Blood Transcriptome Dynamics Following Ischemic Stroke” publication. This study analyzed changes in the dynamics of the peripheral blood transcriptome of human Ischemic Stroke patients. If this script is used, please cite the above paper.

Running the Script

Study analyses were run using Microsoft R Open 4.0.2

By default, the script has the required parameters to recreate the MON (monocyte) Network. Commented in are the parameters required to generate the NEU (neutrophil) and WB (whole blood) networks (only two places need modification: beta1 and kCut). Various output files will also need name changes (“DynamicsOfIS_MON_Network” to “DynamicsOfIS_NEU_Network”, etc). Input data file name will need to be modified.

Testing for module relationships to Diagnosis and other clinical parameters was conducted in another program - Partek Genomics Suite®. Spearman Correlations and Kruskal-Wallis tests were used to determine continuous and categorical parameters’ association with the ModuleEigengeneValues file (noted in the script).

References

WGCNA

Weighted Gene Co-Expression Network Analysis (WGCNA) was first described in the following publications:

-Langfelder P, Horvath S (2008). “WGCNA: an R package for weighted correlation network analysis.” BMC Bioinformatics, 559. https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-9-559.
-Langfelder P, Horvath S (2012). “Fast R Functions for Robust Correlations and Hierarchical Clustering.” Journal of Statistical Software, 46(11), 1-17. https://www.jstatsoft.org/v46/i11/.

CarmonaMoraEtAl_TranscriptomeDynamicsAfterStroke_WGCNA.R

This script is based on and modified from Jeremy Miller’s “Meta-analyses of data from two (or more) microarray data sets” tutorial. The tutorial and related files can be found at: https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/JMiller/. Miller’s tutorial is based on:

-Miller JA, Horvath S, Geschwind DH. (2010) Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways. Proc Natl Acad Sci U S A. 2010 Jul 13;107(28):12698-703.

Additionally, the following resources were utilized in creating this script:

Horvath, S. Weighted Network Analysis. Applications in Genomics and Systems Biology. Book. 2011. https://link.springer.com/book/10.1007/978-1-4419-8819-5

Steve Horvath’s Tutorial “Weighted Gene Co-Expression Network Analysis (WGCNA) R Tutorial” https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/ASPMgene/, which was based on:
-Horvath S, Zhang B, Carlson M, Lu KV, Zhu S, Felciano RM, Laurance MF, Zhao W, Shu, Q, Lee Y, Scheck AC, Liau LM, Wu H, Geschwind DH, Febbo PG, Kornblum HI, Cloughesy TF, Nelson SF, Mischel PS (2006) “Analysis of Oncogenic Signaling Networks in Glioblastoma Identifies ASPM as a Novel Molecular Target”, PNAS | November 14, 2006 | vol. 103 | no. 46 | 17402-17407

Langfelder, P. Signed vs. Unsigned Topological Overlap Matrix. Technical Report. 2013. https://www.researchgate.net/file.PostFileLoader.html?id=57bdeaad40485404eb0753d4&assetKey=AS%3A398680193552384%401472064173254

Langfelder and Horvath’s Tutorial “Network analysis of liver expression data from female mice: finding modules related to body weight”
https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/Tutorials/
https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/Tutorials/FemaleLiver-02-networkConstr-auto.pdf
https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/Tutorials/FemaleLiver-02-networkConstr-man.pdf
https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/Tutorials/FemaleLiver-02-networkConstr-blockwise.pdf
based on:
-Ghazalpour A, Doss S, Zhang B, Wang S, Plaisier C, et al. (2006) Integrating Genetic and Network Analysis to Characterize Genes Related to Mouse Weight. PLOS Genetics 2(8): e130. https://doi.org/10.1371/journal.pgen.0020130

WGCNA FAQ: https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/faq.html

TutorialFunctions.R

This script was taken from Jeremy Miller’s “Meta-analyses of data from two (or more) microarray data sets” tutorial with a minor addition to automatically output Hub Gene lists for each module (defined as the top 5% most interconnected genes in each module). The tutorial and related files can be found at: https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/JMiller/. Miller’s tutorial is based on:

-Miller JA, Horvath S, Geschwind DH. (2010) Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways. Proc Natl Acad Sci U S A. 2010 Jul 13;107(28):12698-703.