Steven Horvath discusses weighted gene co-expression network analysis. This is part of the 2013 UCLA Human Genetics Network Course.
Пікірлер: 16
@Pongant3 жыл бұрын
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-ze7tl2dw4i4 жыл бұрын
just want to say great lecture. really appreciate this.
@EdoardoMarcora9 жыл бұрын
Thanks for putting the course material and lecture online! One question... is the second part of this lecture available as a video on KZbin?
@eabpajarillo10 жыл бұрын
I still haven't finished but the introduction looks so promising. I will watch it later. Hope to have more discussion later.
@nataliec31865 жыл бұрын
Wonderful explanation
@metarasouli5 жыл бұрын
Excellent.
@michielBong4 жыл бұрын
Nice lecture!
@sailing2610 жыл бұрын
Hi. I really enjoyed the lecture. Where do I find the second half? Thank you.
@whiteorchidfaye10 жыл бұрын
really powerful tool!!!
@JohnWayneGao9 жыл бұрын
+whiteorchidfaye are you using it for RNA seq?
@munsifshad82833 жыл бұрын
@@JohnWayneGao you can use it with rna seq after normalization
@HunterDriguez4 жыл бұрын
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.
@emrecaglayan13294 жыл бұрын
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.
@eabpajarillo10 жыл бұрын
Is this better than Cytoscape?
@amrendrakumar20603 жыл бұрын
Sir I need next part video links
@khas-erdenebattogtokh1109 Жыл бұрын
kzbin.info/www/bejne/apmTkJywhZuUmJI here you go mate