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

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

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Күн бұрын

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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Пікірлер: 45

@adepojuayodeji4847 Жыл бұрын

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

@alirezaebaditabrizi7311 11 ай бұрын

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

@irodasay3448 2 жыл бұрын

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

@dyl4nm4rsh4ll Жыл бұрын

eloquently explained, thank you!

@ghadeeralkurdi174 11 ай бұрын

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

@aditimehta4886 Жыл бұрын

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

@karinchansan 11 ай бұрын

Very informative video! Thank you!

@burakkahveci4123 Жыл бұрын

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

@cibelebandeira2625 Ай бұрын

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

@tulikabhardwaj484 Жыл бұрын

Thanks a lot . Extremely helpful

@user-db5yx3fi4u Жыл бұрын

looking forward to your following video, it is really really nice!

@Bioinformagician Жыл бұрын

It's in the works! :)

@user-yh6jr5lu4g 7 ай бұрын

Thank you, this was very helpful!

@tatianas5637 3 ай бұрын

Excellent! Thank you!

@sarrahrose8077 Жыл бұрын

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

@BilalAhmad-gb7ui 2 жыл бұрын

Great effort

@cometellier7983 4 ай бұрын

Thank you let's start !

@sijinakp2744 2 жыл бұрын

Thank you Maam.

@robertoatallah2658 Жыл бұрын

Thanks very clear

@ishakyusuf4616 2 жыл бұрын

perfect! thx

@Cesar06100090 Жыл бұрын

Thanks 🎉

@javierhernando5063 Жыл бұрын

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

@pariaalipour61 Ай бұрын

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

@MGRVE 5 ай бұрын

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

@ajaypradhan5622 Жыл бұрын

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

@davidepasini3807 2 жыл бұрын

Nice video! there is some chance to see in future a video on metilation Array analysis?

@Bioinformagician 2 жыл бұрын

Yes, definitely

@bergerjuliette617 Жыл бұрын

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) ?

@heresmyname-jz8ft Жыл бұрын

any luck with this on youtube maybe?

@mayconmarcao4554 2 жыл бұрын

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.

@Bioinformagician 2 жыл бұрын

I didn't know WGCNA could be used for DNA methylation until I came across a recently published article. So to answer your question, yes WGCNA can be used for methylation data.

@juanete69 Ай бұрын

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

@yeeshouwwang682 2 жыл бұрын

This was extremely helpful. Would you have any recommended resource on adjusting for batch affects and outlier samples from a data set? The samples I have are not well clustered in PCA and their sample correlations are not well defined either, so it seems adjustments will need to be made. Best.

@stretch8390 Жыл бұрын

Have you tried the bioconductor package SVA? You can model known batch effects or unknown through surrogate variable analysis. What type of dataset also? PCA is only one of many types of dimension reduction

@Bioinformagician Жыл бұрын

Have you tried hierarchical clustering to detect outlier samples? Another method in addition to PCA to deal with batch effects is ComBat_seq.

@yeeshouwwang682 Жыл бұрын

I'll definitely give these methods a a try, thank you for the help!

@yongdongwu8472 Жыл бұрын

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,

@Bioinformagician Жыл бұрын

WGCNA can be applied to scRNA-Seq data

@whatamess8764 Жыл бұрын

Thank you for your video. Can we use WGCNA on multiple timepoints? I have 7 timepoints and I'm not sure if it is okay to put them all into one WGCNA analysis or separate out the timepoints and run it separately on each of the timepoints

@Bioinformagician Жыл бұрын

If your goal is to find genes associated with a particular time point then you could perform WGCNA for each time point individually. Another consideration would be how many samples you have, if you have fewer samples for each time point, then constructing one network with all samples make more sense.

@whatamess8764 Жыл бұрын

@@Bioinformagician thank you so much! I have a quick follow up. If we have rpkm values, do we log transform and then normalize? Or do we just log transform? If we normalize, do we normalize first or take the log of the values first? Thank you and sorry for bothering you with these questions!

@md.ishtiakrashid1523 Жыл бұрын

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

@Bioinformagician Жыл бұрын

Yes, WGCNA can be performed on 16S amplicon sequencing data.

@md.ishtiakrashid1523 Жыл бұрын

@@Bioinformagician Thanks a lot. One more thing is that, shall i use it on raw fastq counts or the taxonomy results (OTU table?). Can you enlighten me please?