DESeq2 Design Explained: Understanding Interaction Terms in RNA-Seq Analysis

[@Spirrie2002]

Some of the best teaching content on KZbin! Thank you so much and well done! 😊

[@AvniMann-p4m]

i just love your videos please upload videos on population structure analysis and creating plot of population structure using fast-structure only with huge SNP dataset of about 10 lakh SNPs

[@amirtemer721]

I have to put your channel in my CV 😂. Thank you so much ^^

[@mayconmarcao4554]

Very interesting and important topic. deseq2 is really powerful but requires different levels of understanding to be used properly. What do you mean by “doesn’t affect the fit” ? In case of a DE analysis can the DEGs be different depending the order we specify the factors in the design matrix? - not even talking about interaction terms Thanks !

[@SinergiasHolisticas]

Love it!!!!!!!!!

[@lexieoppong2946]

Thank you for this video! Very helpful! I have a question, if you do not mind. What would the design look like if you are controlling for multiple treatments? Would it be something like this: design = ~treatment1+treatment2+treatment3+genotype

[@lanternofthegreen]

Yes, but put "genotype" at the beginning. And also if you are interested in interaction terms, it would be: design = ~ genotype + treatment1 + treatment2 + treatment3 + genotype:treatment1 + genotype:treatment2 + genotype:treatment3 If you are applying each treatment separately though, meaning that your design matrix looks something like this: genotype treatment sample1 I NONE sample2 II treatment1 sample3 I treatment2 sample4 II treatment3 then you just do genotype+treatment+genotype:treatment as shown in the video.

[@qwerty11111122]

0:10 what is this molecule? CH3OF?

[@a.k.nikson3987]

Great !

[@HominidPetro]

In case 1, you said DESeq2 fits the count data to a linear model. Did you mean a negative binomial model?

[@suspect_device88]

The model that is fit to the counts data is a negative binomial generalised linear model which is still a linear model.

[@lanternofthegreen]

I always do "a + b + a:b" and pick the results I want from it. Trying to work with a+b or a:b alone confuses me.

[@qwerty11111122]

As you should usually, unless a:b is very small and not significant, then just use a+b. Simpler equations are subjectively better. You can use "a*b" as shorthand for "a + b + a:b". It makes it a lot shorter when you have an experiment like "a*b*c", short for "a+b+c+a:b+a:c+b:c+a:b:c"

[@lanternofthegreen]

@@qwerty11111122 Damn I didn't know that. Thanks a lot!

[@arezoorahimi4792]

Hi Thanks for your great channel! I am working on identifying B cell clusters in peritoneum. Could you please provide me gene markers to identify these subpoulations? Thanks