Weighted Gene Co-expression Network Analysis (WGCNA) Step-by-step Tutorial - Part 2

[@manfie290198]

Mad how you managed to make such a cohesive tutorial to this awesome method in under an hour. Thanks a lot!

[@mocabeentrill]

Thank you for the comprehensive tutorial🙏. You're the best!

[@saratavallaei]

This is one of the best tutorials about WGCNA , suggesting it to anyone interested in network analysis. Thank you so much!!

[@072sakibsarker7]

Thank you so much. I learned a lot of things from your videos.

[@anjanamohan5352]

thank you very much from the bottom of heart

[@keshavprasad6485]

Excellent Tutorial. Great Effort. Thanks.

[@jamesgalante993]

Damn -- this tutorial is awesome, thank you so much!

[@mailenortega2212]

Thank you very much for sharing your knowledge, this video was very useful for me. 😉

[@cometellier7983]

Thank you so much, I'll try to understand it but it'll be so helpful I think !

[@ahmedal-mammari9639]

You're the best!

[@Jiwon7-u9v]

Thank you so much for the video! Really helpful. May I please ask, to select for hub gene of a module, do I just have to look at the gene with the lowest pvalue in the module?

[@carolinamartini9850]

amazing tutorial

[@hourirazavi873]

That was great. Thank you so much

[@saeedkhazayel7779]

Hello. Thank you for your good explanation. Is it possible to merge Multiple GSEs which is performed under the same GPL and run this test after that?

[@hunny1215]

thanks a lot for this tutorial.

[@AbhishekSingh-qu5qr]

Hi, Thank you for such a nice tutorial. Could you please also add a section here or in your codes to get the verboseBoxplot for the module of interest or all the modules. That will be very helpful. Thank you in advance.

[@mazb33]

Thank you for such a nice and detailed video; Can you please answer how can we use the "chooseTopHubInEachModule" function? Thanks

[@akhileshmishra9616]

Hi, This is really useful. Great job! One query, How we can use the end result i.e. list of genes correlated to specific phenotypes to build a gene regulatory network?

[@KN-tx7sd]

excellent

[@iiminute4217]

thank you for this amazing video, I just happen to have a doubt, that i could not clarify from internet, In the inputgene expression matrix can we also include wildtype samples along with disease condition, like in diffrenetial expression analysis. My guess is No, but i just wanted to confirm it.

[@nataliagarcia5404]

very useful tutorial! Are there any methods integrating topology analysis of metabolic pathways with wgcna?

[@saraalidadiani5881]

Thank you for the helpful video; when we want to relate the trait file, is it essential that covid be 1 and the rest zero, or can it be vice versa(covid be 0 and healthy 1)? Will it make change the results?

[@Bioinformagician]

It can be vice-versa. The order of encoding should not change the result

[@saraalidadiani5881]

Thank you again for an excellent video. May you please explain how we have to choose the numbers for minModuleSize and maxBlockSize in blockwiseModules? thank you in advance, looking forward to hearing from you!

[@freezingtolerance7493]

Hello, I am dealing with RNA-seq data wherein only treatment name and corresponding expression data. Thus, I do not have any traits. In this case, I cannot calculate p-values? you made heatmap based on traits you are interested. but, can I put treatment names instead of traints?

[@sonaaritra]

Hello, is it possible to download the gene to gene Pearson correlation data for all genes that are present module turquoise? Is there any command for that? I previously used rcorr() function to get gene expression correlation matrix from any given datasets. So, just thinking if it is possible to download a similar matrix for individual module of any WGCNA analysis.

[@Hartecky]

Thank you for your work and tutorial here, I have a question: How to interpret negative correlation of a module with trait ? Let's say MEyellow has correlation of -0.66 (also statistically significant) to disease_state_bin. Thank you in advance for you answer

[@Bioinformagician]

It the trait is of continuous type, then it means gene expression follow the opposite trend to that of trait value. Higher the trait value, lower the gene expression and vice versa. However in case of categorical traits (like disease_state_bin), it just indicates that the difference in gene expression between two groups is significantly different.

[@fearfullywonderfullymade824]

Hi, Can we do WGCNA for a whole genome CRISPR screen

[@drgutharajasekar6275]

why Module eigenegene is called first conponent of PCA and why this First component is required in WGCNA. Average gene expression is of a module is not enough for ME caluculation.

[@ritikasingh8809]

in the heatmap the negative values represents??

[@xinyiliu9909]

Thank you for the practical showcase! I wonder if we can use kendall's correlation instead of the default pearson's correlation for module.trait.corr? Look forward to your answers! :)