Hello, Would someone please help me verify if i'm on the right track in correcting for technical replicates using limma and also if there is a better approach to use (duplicateCorrelation() vs lmer()) for my protein expression data.

I've got 8 disease patients per group (DiseaseA, DiseaseB). There are 2 biological samples(_1,_2) with 4 technical replicates each (rep1-4).

Quite confused on what to include in the block when handling technical replicates - is it the column - patientID or replicates_1 or replicates_2 below. Please note I have manually created replicate_2 column

> sample.sheet
                   group   patientID replicates_1 replicates_2
258736519273_1 Disease_A Disease_A_1            1            1
258736519272_4 Disease_A Disease_A_1            2            2
258736519276_1 Disease_A Disease_A_1            3            3
258736519273_6 Disease_A Disease_A_1            4            4
258736519273_2 Disease_A Disease_A_2            1            1
258736519272_1 Disease_A Disease_A_2            2            2
258736519275_3 Disease_A Disease_A_2            3            3
258736519272_8 Disease_A Disease_A_2            4            4
258736519273_5 Disease_B Disease_B_1            1            5
258736519273_3 Disease_B Disease_B_1            2            6
258736519274_2 Disease_B Disease_B_1            3            7
258736519275_7 Disease_B Disease_B_1            4            8
258736519274_3 Disease_B Disease_B_2            1            5
258736519274_7 Disease_B Disease_B_2            2            6
258736519275_1 Disease_B Disease_B_2            3            7
258736519272_2 Disease_B Disease_B_2            4            8
258736519273_8   Healthy   Healthy_1            1            9
258736519273_4   Healthy   Healthy_1            2           10
258736519272_5   Healthy   Healthy_1            3           11
258736519274_4   Healthy   Healthy_1            4           12
258736519274_8   Healthy   Healthy_2            1            9
258736519276_3   Healthy   Healthy_2            2           10
258736519272_7   Healthy   Healthy_2            3           11
258736519274_5   Healthy   Healthy_2            4           12

group <- factor(sample.sheet$group);
patient <- factor(sample.sheet$patientID); **# unsure whether to use patientID or replicates_1 or replicates_2**

#> table(sample.sheet$group)
#Disease_A Disease_B   Healthy
#        8         8         8

#> table(sample.sheet$patientID)
#Disease_A_1 Disease_A_2 Disease_B_1 Disease_B_2   Healthy_1   Healthy_2
#          4           4           4           4           4           4

# Create a design matrix for your comparison (e.g., control vs treatment)
design <- model.matrix(~0 + group);
colnames(design) <-  gsub('group', '', colnames(design));

# Use duplicateCorrelation to estimate within-patient correlation
corfit <- duplicateCorrelation(exp.data.subset, design, block = patient);

fit <- lmFit(
    exp.data.subset,
    design,
    block = patient,
    correlation = corfit$consensus.correlation
    );

# create contrast matrix
contrast.matrix <- makeContrasts(
    'Disease_AvsHealthy' = Disease_A-Healthy,
    'Disease_BvsHealthy' = Disease_B-Healthy,
    'Disease_A-HealthyvsDisease_B-Healthy' = (Disease_A-Healthy) - (Disease_B-Healthy),
    levels = design
);

fit2 <- contrasts.fit(fit, contrast.matrix);
fit2 <- eBayes(fit2, trend = TRUE); **# robust = TRUE, trend = TRUE? what is more appropriate for proteomics **

The technical samples should be averaged. Then use duplicateCorrelation() with block=PatientID.