So basically I have two genotypes myt1l and wt, and I have two treatments dep and control. The question I want answered is what is the difference between myt1l_dep versus myt1l_control VERSUS wt_dep versus wt_control. Basically we think our genotype is having an impact on the ability of a mouse to respond to a treatment.

I have generated many versions of the contrast but am not sure which one is the best one to use. Here are my top 2 most logical contrasts that I have made so far.

```
#more information about my design
d$samples$group <- paste(d$samples$genotype, d$samples$treatment, d$samples$day, sep = "_")
design <- model.matrix(~0 + group + sex, d$samples)
#... running voom and whatnot ...
contrasts<- makeContrasts(md2_myt1l_dep_v_control_v_wt_dep_v_control = (groupmyt1l_dep_md2 - groupmyt1l_control_md2) +
(groupwt_dep_md2 - groupwt_control_md2),
md2_myt1l_dep_v_control_v_wt_dep_v_control = (groupmyt1l_dep_md2 - groupmyt1l_control_md2) -
(groupwt_dep_md2 - groupwt_control_md2),
levels=colnames(coef(fit)))
contrasts
Contrasts
Levels md2_myt1l_dep_v_control_v_wt_dep_v_control md2_myt1l_dep_v_control_v_wt_dep_v_control
groupmyt1l_control_md2 -1 -1
groupmyt1l_control_md6 0 0
groupmyt1l_dep_md2 1 1
groupmyt1l_dep_md6 0 0
groupwt_control_md2 -1 1
groupwt_control_md6 0 0
groupwt_dep_md2 1 -1
groupwt_dep_md6 0 0
sexm 0 0
design
groupmyt1l_control_md2 groupmyt1l_control_md6 groupmyt1l_dep_md2 groupmyt1l_dep_md6 groupwt_control_md2
sample.27247_3m_myt1lmd6_dep 0 0 0 1 0
sample.27247_3m_myt1lmd6_control 0 1 0 0 0
sample.27247_2m_wtmd6_dep 0 0 0 0 0
sample.27247_2m_wtmd6_control 0 0 0 0 0
sample.27247_1m_wtmd6_dep 0 0 0 0 0
sample.27247_1m_wtmd6_control 0 0 0 0 0
sample.27266_7f_wtmd6_dep 0 0 0 0 0
sample.27266_7f_wtmd6_control 0 0 0 0 0
sample.27266_5f_myt1lmd6_dep 0 0 0 1 0
sample.27266_5f_myt1lmd6_control 0 1 0 0 0
sample.27402_5f_myt1lmd6_dep 0 0 0 1 0
sample.27402_5f_myt1lmd6_control 0 1 0 0 0
sample.27402_3f_wtmd6_dep 0 0 0 0 0
sample.27402_3f_wtmd6_control 0 0 0 0 0
sample.27299_4m_wtmd6_dep 0 0 0 0 0
sample.27299_4m_wtmd6_control 0 0 0 0 0
sample.27299_3m_myt1lmd6_dep 0 0 0 1 0
sample.27299_3m_myt1lmd6_control 0 1 0 0 0
sample.27254_7f_myt1lmd6_dep 0 0 0 1 0
sample.27254_7f_myt1lmd6_control 0 1 0 0 0
sample.27254_2f_myt1lmd6_dep 0 0 0 1 0
sample.27254_2f_myt1lmd6_control 0 1 0 0 0
sample.27247_3f_myt1lmd2_dep 0 0 1 0 0
sample.27247_3f_myt1lmd2_control 1 0 0 0 0
sample.27247_1f_wtmd2_dep 0 0 0 0 0
sample.27247_1f_wtmd2_control 0 0 0 0 1
sample.27266_9f_wtmd2_dep 0 0 0 0 0
sample.27266_9f_wtmd2_control 0 0 0 0 1
sample.27266_8f_myt1lmd2_dep 0 0 1 0 0
sample.27266_8f_myt1lmd2_control 1 0 0 0 0
sample.27402_4m_myt1lmd2_dep 0 0 1 0 0
sample.27402_4m_myt1lmd2_control 1 0 0 0 0
sample.27402_2m_myt1lmd2_dep 0 0 1 0 0
sample.27402_2m_myt1lmd2_control 1 0 0 0 0
sample.27402_1m_wtmd2_dep 0 0 0 0 0
sample.27402_1m_wtmd2_control 0 0 0 0 1
sample.27299_2f_wtmd2_dep 0 0 0 0 0
sample.27299_2f_wtmd2_control 0 0 0 0 1
sample.27299_1f_wtmd2_dep 0 0 0 0 0
sample.27299_1f_wtmd2_control 0 0 0 0 1
sample.27299_5f_myt1lmd2_dep 0 0 1 0 0
sample.27299_5f_myt1lmd2_control 1 0 0 0 0
sample.27254_3m_myt1lmd2_dep 0 0 1 0 0
sample.27254_3m_myt1lmd2_control 1 0 0 0 0
sample.27254_5m_wtmd2_dep 0 0 0 0 0
sample.27254_5m_wtmd2_control 0 0 0 0 1
groupwt_control_md6 groupwt_dep_md2 groupwt_dep_md6 sexm
sample.27247_3m_myt1lmd6_dep 0 0 0 1
sample.27247_3m_myt1lmd6_control 0 0 0 1
sample.27247_2m_wtmd6_dep 0 0 1 1
sample.27247_2m_wtmd6_control 1 0 0 1
sample.27247_1m_wtmd6_dep 0 0 1 1
sample.27247_1m_wtmd6_control 1 0 0 1
sample.27266_7f_wtmd6_dep 0 0 1 0
sample.27266_7f_wtmd6_control 1 0 0 0
sample.27266_5f_myt1lmd6_dep 0 0 0 0
sample.27266_5f_myt1lmd6_control 0 0 0 0
sample.27402_5f_myt1lmd6_dep 0 0 0 0
sample.27402_5f_myt1lmd6_control 0 0 0 0
sample.27402_3f_wtmd6_dep 0 0 1 0
sample.27402_3f_wtmd6_control 1 0 0 0
sample.27299_4m_wtmd6_dep 0 0 1 1
sample.27299_4m_wtmd6_control 1 0 0 1
sample.27299_3m_myt1lmd6_dep 0 0 0 1
sample.27299_3m_myt1lmd6_control 0 0 0 1
sample.27254_7f_myt1lmd6_dep 0 0 0 0
sample.27254_7f_myt1lmd6_control 0 0 0 0
sample.27254_2f_myt1lmd6_dep 0 0 0 0
sample.27254_2f_myt1lmd6_control 0 0 0 0
sample.27247_3f_myt1lmd2_dep 0 0 0 0
sample.27247_3f_myt1lmd2_control 0 0 0 0
sample.27247_1f_wtmd2_dep 0 1 0 0
sample.27247_1f_wtmd2_control 0 0 0 0
sample.27266_9f_wtmd2_dep 0 1 0 0
sample.27266_9f_wtmd2_control 0 0 0 0
sample.27266_8f_myt1lmd2_dep 0 0 0 0
sample.27266_8f_myt1lmd2_control 0 0 0 0
sample.27402_4m_myt1lmd2_dep 0 0 0 1
sample.27402_4m_myt1lmd2_control 0 0 0 1
sample.27402_2m_myt1lmd2_dep 0 0 0 1
sample.27402_2m_myt1lmd2_control 0 0 0 1
sample.27402_1m_wtmd2_dep 0 1 0 1
sample.27402_1m_wtmd2_control 0 0 0 1
sample.27299_2f_wtmd2_dep 0 1 0 0
sample.27299_2f_wtmd2_control 0 0 0 0
sample.27299_1f_wtmd2_dep 0 1 0 0
sample.27299_1f_wtmd2_control 0 0 0 0
sample.27299_5f_myt1lmd2_dep 0 0 0 0
sample.27299_5f_myt1lmd2_control 0 0 0 0
sample.27254_3m_myt1lmd2_dep 0 0 0 1
sample.27254_3m_myt1lmd2_control 0 0 0 1
sample.27254_5m_wtmd2_dep 0 1 0 1
sample.27254_5m_wtmd2_control 0 0 0 1
attr(,"assign")
[1] 1 1 1 1 1 1 1 1 2
attr(,"contrasts")
attr(,"contrasts")$group
[1] "contr.treatment"
attr(,"contrasts")$sex
[1] "contr.treatment"
```

When I use the plus sign, I get something that looks like a normal volcano plot but the logFC's are somewhat hard to interpret. When I use the minus sign all the p-values jump to 1 and the volcano plot looks like a line... Has anyone done this contrast before and knows what to do here? I have looked all over including in this guide: https://bioconductor.org/packages/release/workflows/vignettes/RNAseq123/inst/doc/designmatrices.html but have not found a concrete answer so far... Thanks!

Dividing the contrast by 2 only changes the estimated coefficients, it won't change the p-values.

Oh yes. Of course. Thanks for pointing that out!