Weighted gene co-expression network analysis

Steven Horvath discusses weighted gene co-expression network analysis. This is part of the 2013 UCLA Human Genetics Network Course.

[@Pongant]

This presentation really helped with my Master Thesis. It is so much more informative to work with modules of DEGs than just investigating single genes...

[@user-ze7tl2dw4i]

just want to say great lecture. really appreciate this.

[@eabpajarillo]

I still haven't finished but the introduction looks so promising. I will watch it later. Hope to have more discussion later.

[@EdoardoMarcora]

Thanks for putting the course material and lecture online! One question... is the second part of this lecture available as a video on UA-cam?

[@nataliec3186]

Wonderful explanation

[@metarasouli]

Excellent.

[@michielBong]

Nice lecture!

[@sailing26]

Hi. I really enjoyed the lecture. Where do I find the second half? Thank you.

[@whiteorchidfaye]

really powerful tool!!!

[@JohnWayneGao]

+whiteorchidfaye are you using it for RNA seq?

[@munsifshad8283]

@@JohnWayneGao you can use it with rna seq after normalization

[@HunterDriguez]

I swear, no matter how much I try to intuitively understand eigengenes I fail miserably. I wonder how you go from your expression data to a (eigene?) value for each module at each sample, which is the output you get when you run the code.

[@emrecaglayan1329]

Module eigengene is the first principal component of the genes in that module. In intuitive terms, it is the made-up gene that represents all genes in that module the best. If you want to understand principal component analysis I recommend statquest's video on that on youtube.

[@eabpajarillo]

Is this better than Cytoscape?

[@amrendrakumar2060]

Sir I need next part video links

[@khas-erdenebattogtokh1109]

ua-cam.com/video/4h0_izP6ab0/v-deo.html here you go mate