Weighted Gene Co-expression Network Analysis (WGCNA) Step-by-step Tutorial - Part 2
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- čas přidán 29. 06. 2024
- This is part 2 of step-by-step tutorial of Weighted Gene Co-expression Network Analysis (WGCNA).
In this video I demonstrate how to correlate modules to phenotypes/traits of interest and perform intramodular analysis by identifying hub genes using module measures and calculate gene significance. In addition, I provide an intuition about associating module eigengenes (continuous variable) to a trait of interest (categorical variable). I hope you find this video helpful! I look forward to your comments in the comment section below!
Part 1 of this tutorial:
• Weighted Gene Co-expre...
Data:
www.ncbi.nlm.nih.gov/geo/quer...
Code:
github.com/kpatel427/CZcamsT...)
WGCNA Tutorial:
horvath.genetics.ucla.edu/htm...
Chapters
0:00 Intro
00:03 Relate modules to phenotypes/traits
00:14 Binarize categorical variables (traits)
6:03 Correlate module eigengenes with traits
7:23 Visualize module-trait relationship as a heatmap
12:12 Extract genes from modules of interest
13:36 Does it make sense to use Pearson correlation to correlate a continous variable (module eigengene) with a categorical variable (disease severity)?
16:13 Intramodular analysis: get genes with high module membership (hub genes)
17:30 Intramodular analysis: get genes significantly associated with traits of interest
You can show your support and encouragement by buying me a coffee:
www.buymeacoffee.com/bioinfor...
To get in touch:
Website: bioinformagician.org/
Github: github.com/kpatel427
Email: khushbu_p@hotmail.com
#bioinformagician #bioinformatics #wgcna #coexpressionnetworks #geneexpression #scalefreenetworks #proteinproteininteractionnetworks #sequencing #coverage #samtools #depthofsequencing #samflag #sam #bam #alignment #phred #fasta #fastq #singlecell #10X #ensembl #biomart #annotationdbi #annotables #affymetrix #microarray #affy #ncbi #genomics #beginners #tutorial #howto #omics #research #biology #GEO #rnaseq #ngs
Mad how you managed to make such a cohesive tutorial to this awesome method in under an hour. Thanks a lot!
This is one of the best tutorials about WGCNA , suggesting it to anyone interested in network analysis.
Thank you so much!!
Thank you for the comprehensive tutorial🙏. You're the best!
That was great. Thank you so much
Excellent Tutorial. Great Effort. Thanks.
Damn -- this tutorial is awesome, thank you so much!
Thank you very much for sharing your knowledge, this video was very useful for me. 😉
You're the best!
Thank you so much, I'll try to understand it but it'll be so helpful I think !
amazing tutorial
thanks a lot for this tutorial.
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.
excellent
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!
Thank you for such a nice and detailed video;
Can you please answer how can we use the "chooseTopHubInEachModule" function?
Thanks
very useful tutorial! Are there any methods integrating topology analysis of metabolic pathways with wgcna?
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?
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.
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.
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?
It can be vice-versa. The order of encoding should not change the result
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
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.
in the heatmap the negative values represents??
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?
Hi, Can we do WGCNA for a whole genome CRISPR screen
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.
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! :)