Dear colleagues,

I'm analyzing a dataset with both within- and between-individuals comparisons. I'm insecure about some of contrasts and wondered if someone more experienced can take a look.

About the target variables:

disease = between-subject (disease, control)
treatment = within-subject (after, before)
clin = between-subject (symptomatic, asymptomatic)

To reproduce my pdata.

Below there's an excerpt of the code.

Any input is appreciated. Thank you!

y <- DGEList(counts = txi$counts, samples = pdata)

# collapse 3 factor vars. into one
pdata$groups <- with(pdata, paste(disease, severity, treat, sep = "_"))

y <- y[filterByExpr(y, design = model.matrix(~groups, metadata)), ]
dge1 <- calcNormFactors(y)

# model matrix without intecept
design1 <- model.matrix(~0+groups+sex+age+bmi, dge1$samples)

v <- voomWithQualityWeights(dge1, design1, plot = TRUE)

# random effects for patient, 1st pass
corfit <- duplicateCorrelation(v, design1, block = dge1$samples$pat_id)
corfit$con

v <- voomWithQualityWeights(dge1, design1, block = dge1$samples$pat_id, 
          correlation = corfit$consensus)

corfit <- duplicateCorrelation(v, design1, block = dge1$samples$pat_id)
corfit$con

fit1 <- lmFit(v, design1, block = dge1$samples$pat_id, 
              correlation = corfit$consensus.correlation)    

# Contrasts 

cont1 <- makeContrasts(
        Disease_Symptom = groupsDISEASE_SYMPTOMATIC_AFTER - # Disease after vs before treat (symptomatic)
                groupsDISEASE_SYMPTOMATIC_BEFORE, 

        Control_Asymptom = groupsCONTROL_ASYMPTOMATIC_AFTER - # Control after vs. before treat (asymptom)
                groupsCONTROL_ASYMPTOMATIC_BEFORE, 

        Disease_Asymptom = groupsDISEASE_ASYMPTOMATIC_AFTER - # Disease after vs. before treat (asympto)
                groupsDISEASE_ASYMPTOMATIC_BEFORE, 

        Treat_Diff_Disease_Symptom =  # Difference in treatment response between disease symptomatic vs asymptomatic (diff of diff)
                (groupsDISEASE_SYMPTOMATIC_AFTER -
                         groupsDISEASE_SYMPTOMATIC_BEFORE) -
                (groupsDISEASE_ASYMPTOMATIC_AFTER - 
                         groupsDISEASE_ASYMPTOMATIC_BEFORE), 

        Disease_Symp_Control = ( # Difference in treatment response between disease symptom vs control asymptom (diff of diff)
                groupsDISEASE_SYMPTOMATIC_AFTER -
                        groupsDISEASE_SYMPTOMATIC_BEFORE) -
                (groupsCONTROL_ASYMPTOMATIC_AFTER - 
                         groupsCONTROL_ASYMPTOMATIC_BEFORE), 

        Eff_Treat = ( # Average efect of treatment regardless of symptom and disease
                groupsDISEASE_SYMPTOMATIC_AFTER +
                        groupsCONTROL_ASYMPTOMATIC_AFTER)/2
        -
                (groupsDISEASE_SYMPTOMATIC_BEFORE +
                         groupsCONTROL_ASYMPTOMATIC_BEFORE)/2,


        levels = design1)


fit2 <- contrasts.fit(fit1, cont1)

fit2 <- eBayes(fit2, robust = TRUE)

colnames(fit2$coefficients)

knitr::kable(summary(decideTests(fit2, adjust.method = "BH", p.value = 0.1)), format = "simple")

#           Disease_Symptom   Control_Asymptom   Disease_Asymptom   Treat_Diff_Disease_Symptom   Disease_Symp_Control   Eff_Treat
# -------  ----------------  -----------------  -----------------  ---------------------------  ---------------------  ----------
# Down                   27                151                  0                          178                      0         952
# NotSig              14134              13753              14417                        13980                  14417       11983
# Up                    256                513                  0                          259                      0        1482

The coefficients seem to agree with graphical EDA and descriptive summaries.

Your interpretations seem to match your contrast definitions.