Introduction to Weighted Gene Co-expression Network Analysis (WGCNA) | Bioinformatics 101

Weighted Gene Co-expression Network Analysis (WGCNA) is a commonly used unsupervised method to cluster genes based on their expression profiles. In this video I go over the idea behind WGCNA and provide a high-level overview of various steps that go into this analysis. In addition, I talk about good practices when performing WGCNA. I hope you find this video helpful! Leave your thoughts in the comment section below!

Chapters:
0:00 Intro
1:01 Studying systemic response
3:04 Basic idea behind WGCNA
4:00 Examples of functional association among co-expressed genes
6:27 Terminologies
7:44 WGCNA workflow steps (high level overview)
9:19 WGCNA Applications
9:54 Good practices

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[javierhernando]

Thank you so much for the video! Good job! Do you think it could be applied to RTqPCR gene expression values of genes that are known to be "connected"? I would be working with a matrix as well

[pariaalipour]

Thanks for the helpful video! I was wondering how come batch effect correction is useful when we use the expression matrix as input?

[juanete]

How do you know it's not the opposite direction? How do you know that gene2 is not causing higher gene expression of gene1?

[inl]

Thank you so much for making this video!!!

[ajaypradhan]

Great video... Just wondering. now it seems using both DEGs and WGCNA is becoming more common. How would you justify this?

[sakibsarker]

How can I know whether my dataset has batch effect or not?

[jaeeponde]

hi!! can i download these slides anywhere?

[irodasay]

That's what I was looking for, Thank you so much.

[MGRVE]

Thanks for the video. I keep struggling with the rationale of WGCNA when applied in case-control studies: As typically, in a case-control setting, not that many independent repeats are performed that could produce a meaningful correlation statistic, the overwhelming difference will originate from the original contrast: case-control. Hence, differential gene expression will essentially determine the correlated genes (as the major perturbation is the case-control difference), and thus, differential gene expression is what we are obtaining after all. Dirk

[adepojuayodeji]

This was helpful and a good foundation for the mathematical demonstration that is awaited. Hope you get to drop the video of the steps to perform the WGCNA soon. Thanks

[cibelebandeira]

Thank you for this amazing video, slides and explanation!!

[ghadeeralkurdi]

Fantastic job. Thanks for sharing. It is very helpful.

[sarrahrose]

Thank you for the video - I'm an 18yo learning bioinformatics and this has been a very effective introduction (_:

[aditimehta]

Hey Khusbhu! Really helpful video and very well explained. Thank you :)

[burakkahveci]

Thank you for this video, this is perfect video for the introduction

[yongdongwu]

it's very helpful, and i want know, can this method can apply to sc-RNA seq data that i got two different treated tissue, or only apply to bulk RNA and mircoarray, thank you,

[mayconmarcao]

loved this theoretical-first approach. It clarified several of my doubts.
Do you think WGCNA also works for DNA methylation? (e.g: filtering features). Thanks.

[alirezaebaditabrizi]

Excellent, Thank you so much for sharing this tutorial. Hope to see more of you.

[bergerjuliette]

Thank you very much for this tutorial!!! could you explain and propose a workflow on weighted topological overlap (wTO) package and or / CoDiNA (Co-expression Differential Network Analysis) ?

[md.ishtiakrashid]

Is it possible to perform WGCNA analysis from 16S amplicon sequencing data? Although I have done picrust2 prediction. so i am curious.