What is the difference between:

A) running voom() + duplicateCorrelation() once or; 

B) running voom() + duplicateCorrelation() + voom() + duplicateCorrelation(), twice each?

I'm getting more shrunken log2FC (shrunken towards 0) when running case B (see code below).

About the experiment

I have an unbalanced design in which I need to make both between-patient comparison (Form of disease) and within-patient comparison (factor: no. months receveing treat. dose, repeated measurements).

This is my formula and design matrix: 

Code

design <- model.matrix(~libSize + Class + Sex + Doses, data = pdat)

design <-
structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 
0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 
1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 
1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 
1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 
1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 
1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 
0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 
0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 
0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 
0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0), .Dim = c(61L, 10L), .Dimnames = list(c("1", "2", 
"3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", 
"15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", 
"26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", 
"37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", 
"48", "49", "50", "51", "52", "53", "54", "55", "56", "57", "58", 
"59", "60", "61"), c("(Intercept)", "libSize(17,32]", "ClassLL", 
"SexM", "Doses3", "Doses4", "Doses6", "Doses9", "Doses10", "Doses12"
)), assign = c(0L, 1L, 2L, 3L, 4L, 4L, 4L, 4L, 4L, 4L), contrasts = list(
    libSize = "contr.treatment", Class = "contr.treatment", Sex = "contr.treatment", 
    Doses = "contr.treatment"))

Case A)

y <- DGEList(txi$counts)
y <- y[filterByExpr(y), , keep.lib.sizes = FALSE]
y <- calcNormFactors(y)
design <- model.matrix(~libSize + Class + Sex + Doses, data = pdat)
v <- voom(y, design, plot = TRUE, normalize.method = "quantile")
corfit <- duplicateCorrelation(v, design, block = pdat$Patient)

fit <- lmFit(v,
              design,
              block = pdat$Patient,
              correlation = corfit$con)

fit2 <- eBayes(fit, robust = TRUE)
res <- topTable(fit2, coef = 3, number = Inf, adjust.method = "BH", confint = TRUE)
hist(res$P.Value)
summary(decideTests(fit2, adjust.method = "fdr", p.value = 0.1, lfc = 1))

case B) 

y <- DGEList(txi$counts)
y <- y[filterByExpr(y), , keep.lib.sizes = FALSE]
y <- calcNormFactors(y)
design <- model.matrix(~libSize + Class + Sex + Doses, data = pdat)
v <- voom(y, design, plot = TRUE, normalize.method = "quantile")
corfit <- duplicateCorrelation(v, design, block = pdat$Patient)
v <- voom(v, design,
                plot = TRUE,
                block = pdat$Patient,
                correlation = corfit$consensus.correlation)

corfit <- duplicateCorrelation(v, design, block = pdat$Patient)

fit <- lmFit(v,
              design,
              block = pdat$Patient,
              correlation = corfit$con)

fit2 <- eBayes(fit, robust = TRUE)
res <- topTable(fit2, coef = 3, number = Inf, adjust.method = "BH", confint = TRUE)
hist(res$P.Value)
summary(decideTests(fit2, adjust.method = "fdr", p.value = 0.1, lfc = 1))

Number of DE according to:

summary(decideTests(fit2, adjust.method = "fdr", p.value = 0.1, lfc = 1))

A)

      (Intercept) libSize(17,32] ClassLL  SexM Doses3 Doses4 Doses6
Down             0             37       9    14      0      1      1
NotSig         800          14275   14298 14271  14235  14318  14126
Up           13519              7      12    34     84      0    192
       Doses9 Doses10 Doses12
Down        7     115       1
NotSig  14309   13982   14318
Up          3     222       0

B)

       (Intercept) libSize(17,32] ClassLL  SexM Doses3 Doses4 Doses6
Down             0              0       1     1      1      0      0
NotSig           0          14242   14241 14235  14239  14241  14235
Up           14242              0       0     6      2      1      7
       Doses9 Doses10 Doses12
Down       10      10       0
NotSig  14213   14197   14242
Up         19      35       0

Thank you.

Regards,

Thyago

------------------------------
> sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.1 LTS

Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1

locale:
 [1] LC_CTYPE=pt_BR.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=pt_BR.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=pt_BR.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=pt_BR.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=pt_BR.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] parallel  stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] bindrcpp_0.2.2              pheatmap_1.0.10            
 [3] WriteXLS_4.0.0              cowplot_0.9.3              
 [5] ggplot2_3.1.0               dplyr_0.7.7                
 [7] DESeq2_1.20.0               SummarizedExperiment_1.10.1
 [9] DelayedArray_0.6.6          BiocParallel_1.14.2        
[11] matrixStats_0.54.0          Biobase_2.40.0             
[13] GenomicRanges_1.32.7        GenomeInfoDb_1.16.0        
[15] IRanges_2.14.12             S4Vectors_0.18.3           
[17] BiocGenerics_0.26.0         readxl_1.1.0               
[19] edgeR_3.22.5                limma_3.36.5               

loaded via a namespace (and not attached):
 [1] bit64_0.9-7            splines_3.5.1          Formula_1.2-3         
 [4] assertthat_0.2.0       statmod_1.4.30         latticeExtra_0.6-28   
 [7] blob_1.1.1             GenomeInfoDbData_1.1.0 cellranger_1.1.0      
[10] yaml_2.2.0             pillar_1.3.0           RSQLite_2.1.1         
[13] backports_1.1.2        lattice_0.20-35        glue_1.3.0            
[16] digest_0.6.18          RColorBrewer_1.1-2     XVector_0.20.0        
[19] checkmate_1.8.5        colorspace_1.3-2       htmltools_0.3.6       
[22] Matrix_1.2-14          plyr_1.8.4             XML_3.98-1.16         
[25] pkgconfig_2.0.2        genefilter_1.62.0      zlibbioc_1.26.0       
[28] purrr_0.2.5            xtable_1.8-3           scales_1.0.0          
[31] htmlTable_1.12         tibble_1.4.2           annotate_1.58.0       
[34] withr_2.1.2            nnet_7.3-12            lazyeval_0.2.1        
[37] cli_1.0.1              survival_2.42-6        magrittr_1.5          
[40] crayon_1.3.4           memoise_1.1.0          fansi_0.4.0           
[43] foreign_0.8-71         tools_3.5.1            data.table_1.11.8     
[46] stringr_1.3.1          munsell_0.5.0          locfit_1.5-9.1        
[49] cluster_2.0.7-1        AnnotationDbi_1.42.1   compiler_3.5.1        
[52] rlang_0.3.0.1          grid_3.5.1             RCurl_1.95-4.11       
[55] rstudioapi_0.8         htmlwidgets_1.3        bitops_1.0-6          
[58] base64enc_0.1-3        gtable_0.2.0           DBI_1.0.0             
[61] R6_2.3.0               gridExtra_2.3          knitr_1.20            
[64] utf8_1.1.4             bit_1.1-14             bindr_0.1.1           
[67] Hmisc_4.1-1            stringi_1.2.4          Rcpp_0.12.19          
[70] geneplotter_1.58.0     rpart_4.1-13           acepack_1.4.1         
[73] tidyselect_0.2.5  

Re-running both commands is the recommended approach. The first pass through voom is less accurate because it doesn't know about the correlations between samples from the same patients. Instead, it will assume that those samples are independent, which will overstate the contribution of patients that have many samples and reduce the precision of the coefficient estimates. By repeating the voom step with the consensus correlation, we obtain more accurate model fits and thus more appropriate observation weights. This feeds back to improve accuracy for the second duplicateCorrelation step, which depends on the weights.

Of course, this assumes that the initial linear model fits were not too wrong, otherwise iteration might amplify errors instead. I'm not aware of any theoretical guarantees about convergence towards the best estimates.

limma-voom does the same amount of logFC shrinkage whether you run it one or twice. Of course the results will not be exactly the same, and the number of DE genes might go up or down when you run voom + duplicateCorrelation the second time. As Aaron has already said, we recommend the re-run.

Also, quoting from the help page for decideTests:

"Note. Although this function enables users to set p-value and lfc cutoffs simultaneously, this combination criterion is not usually recommended."