WGCNA in a nutshell

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A quick bitesize intro about Weighted Gene Co-expression Network Analysis (WGNCA), it's quite common yet important for the network analysis of genomic data.
References:
1) Tutorial in R horvath.genetics.ucla.edu/htm...
2) Dr Laura Saba has a detailed and interesting webinar
• Webinar #7 - Introduct...
Connect with me:
Email: liquidbrain.r@gmail.com
Github: github.com/Lindseynicer
Twitter: / lianfoong
More information:
bit.ly/LiancheeFoong
Email: liquidbrain.r@gmail.com
Website: www.liquidbrain.org/videos
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Опубликовано:

30 июл 2024

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Комментарии : 29

@eaturfeet653 3 года назад

This clears up so much for me! I just recently started getting deep in to transcriptomic analysis for my PhD. Thank you for this!

@Unaimend 2 года назад

Thank you for taking your time and giving an introduction to WGCNA :)

@mightyowl1668 2 года назад

Amazing video! Please keep making these!!

@shinjayson6432 Год назад

This is wonderful. Thank you very much.

@ruchichauhan5412 3 года назад

This was very helpful! Thanks a lot

@asiyazhao3820 3 года назад

Hi thanks for your video. just a quick question, do you adjust p-values for spearman correlation before you merge the correlation data? thank you very much

@angelac.1653 2 года назад

This video was great, thank you so much for making it! I've been trying to apply transcriptional gene modules to a project I'm working on. The approach I read about (Deep Extraction Independent Component Analysis) used ICA and artificial neural networks to identify the modules (the authors created the DEXICA package for R). WGCNA is an interesting comparison.

@LiquidBrain 2 года назад

Glad it was helpful! The approach you mentioned looks new to me, will be great if it improves/overperforms the statistical algorithm by WGCNA

@xudongwang-je6vo 3 месяца назад

YES, IT IS VERY VERY HELPFUL FOR ME, THANK YOU!!! SO MUCH!!!

@KN-tx7sd 2 года назад

Thank you, this is an excellent and clear presentation, mentioning the strengths and weaknesses of WGCNA. Can clarify whether WGCNA could be applied to metabolomics datasets. I'm seeing more and more papers using this approach. Do we need a minimum number of samples in such datasets and do we need to make any tweaks in the software to accommodate metabolites and lastly how could the interpretation be different from gene expression networks.

@MrRamaeri 3 года назад

thank you

@grsbiosciences 2 года назад

Great explanation, how to know which module has genes related to a.particular phenotype. So how I relate my modules to traits in my case

@LiquidBrain 2 года назад

Hi good question, you may refer to my tutorial videos. Part 1: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-dwWhm78j8YU.html , Part 2: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-5Vdp9Om3gAg.html Happy learning! - Lind

@jamesgalante993 2 года назад

Thanks!

@grsbiosciences 2 года назад

Madam I have gene expression data of control and drug treated, what I can find with WGCNA for my data

@LiquidBrain 2 года назад

Hi you may try to explore more about its application from the official website: horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/

@ryankelly9302 3 года назад

Great video! Do you guys plan to do a tutorial on the WGCNA R package? Think it would go great with this video if you were able to use it with some RNA-seq data.

@LiquidBrain 3 года назад

Ya, it's actually in the pipeline way before this video. But a full pipeline takes a lot of plan , film, edit and stuff~but hopefully we can get it done soon

@ryankelly9302 3 года назад

@@LiquidBrain Great to hear! I look forward to that video!!

@jianjiang8234 3 года назад

@@LiquidBrain I'm also looking forward to the video.

@aayushinotra5775 2 года назад

Hi can you please tell me how to install WGCNA R package. It would be great if you could help

@LiquidBrain 2 года назад

Hi here's the script for installation: install.packages("BiocManager") BiocManager::install("WGCNA") you may refer to my tutorial videos too. Part 1: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-dwWhm78j8YU.html , Part 2: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-5Vdp9Om3gAg.html Happy learning! - Lind

@Unaimend 2 года назад

To which paper are referring to at 8:40?

@LiquidBrain 2 года назад

Hi Thomas, did you refer to the dynamic tree cut? I got it from the official WGCNA tutorial website (horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/Tutorials/FemaleLiver-02-networkConstr-auto.pdf) -Lind

@OrlandoG 3 года назад

Very good informatiion. Thank you for sharing this. 👍 Please don´t take this the wrong way, I know english isn´t your first language, but I find it difficult to understand your sentences at times because of your pronunciation. Thanks again and I look forward to more videos.

@LiquidBrain 3 года назад

Thanks for your feedbacks. It brings me pure joy whenever I’ve finally understood something and able to share it around! I’m working to put up the subtitle there and glad that you found this video helpful :)

@PeihuiBrandonYeo 3 года назад

interesting

@WalyB01 2 года назад

Bad rehash of some else her video. Not cool using some else her slide!

@lindseyliquidbrain7139 2 года назад

We all are learning along the way. I did not rehash but try to summarize what I've learned after taking a long time to digest the formal knowledge sources (e.g. publication, tutorials, or webinar). You could just go ahead to read and study via the original sources as I cited above if you preferred.