Hi

I'm comparing the outputs of the betweenLaneNormalization(which="upper") function and the calcNormFactors(method="upperquartile") function. Technically both functions are based on the same method from Bullard. But I get completely opposite library sizes after I perform the normalization. Which one is right? Am I doing something wrong?

library(RUVSeq)
library(edgeR)
library(zebrafishRNASeq)
data(zfGenes)

filter <- apply(zfGenes, 1, function(x) length(x[x>5])>=2)
filtered <- zfGenes[filter,]
genes <- rownames(filtered)[grep("^ENS", rownames(filtered))]
spikes <- rownames(filtered)[grep("^ERCC", rownames(filtered))]

x <- as.factor(rep(c("Ctl", "Trt"), each=3))
set <- newSeqExpressionSet(as.matrix(filtered),
                           phenoData = data.frame(x, row.names=colnames(filtered)),)

set <- betweenLaneNormalization(set, which="upper",offset = T)
![enter image description here][1]
libsizes1 <- colSums(set@assayData$normalizedCounts)

design <- model.matrix(~x, data=pData(set))
y <- DGEList(counts=counts(set), group=x)
y <- calcNormFactors(y, method="upperquartile")

libsizes2 <- y$samples$lib.size*y$samples$norm.factors

plot(libsizes1,libsizes2)

    sessionInfo( )
R version 4.2.0 (2022-04-22 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19045)

Matrix products: default

locale:
[1] LC_COLLATE=English_United Kingdom.utf8 
[2] LC_CTYPE=English_United Kingdom.utf8   
[3] LC_MONETARY=English_United Kingdom.utf8
[4] LC_NUMERIC=C                           
[5] LC_TIME=English_United Kingdom.utf8    

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

other attached packages:
 [1] zebrafishRNASeq_1.18.0      RUVSeq_1.32.0              
 [3] EDASeq_2.32.0               ShortRead_1.56.1           
 [5] GenomicAlignments_1.34.1    SummarizedExperiment_1.28.0
 [7] MatrixGenerics_1.10.0       matrixStats_1.2.0          
 [9] Rsamtools_2.14.0            GenomicRanges_1.50.2       
[11] Biostrings_2.66.0           GenomeInfoDb_1.34.9        
[13] XVector_0.38.0              IRanges_2.32.0             
[15] S4Vectors_0.36.2            BiocParallel_1.32.6        
[17] Biobase_2.58.0              BiocGenerics_0.44.0        
[19] edgeR_3.40.2                limma_3.54.2               

loaded via a namespace (and not attached):
 [1] bitops_1.0-7           bit64_4.0.5           
 [3] filelock_1.0.3         RColorBrewer_1.1-3    
 [5] progress_1.2.3         httr_1.4.7            
 [7] tools_4.2.0            utf8_1.2.4            
 [9] R6_2.5.1               DBI_1.2.2             
[11] tidyselect_1.2.0       prettyunits_1.2.0     
[13] bit_4.0.5              curl_5.2.1            
[15] compiler_4.2.0         cli_3.6.2             
[17] xml2_1.3.6             DelayedArray_0.24.0   
[19] rtracklayer_1.58.0     rappdirs_0.3.3        
[21] stringr_1.5.1          digest_0.6.34         
[23] rmarkdown_2.25         R.utils_2.12.3        
[25] jpeg_0.1-10            pkgconfig_2.0.3       
[27] htmltools_0.5.7        dbplyr_2.4.0          
[29] fastmap_1.1.1          rlang_1.1.3           
[31] rstudioapi_0.15.0      RSQLite_2.3.5         
[33] BiocIO_1.8.0           generics_0.1.3        
[35] hwriter_1.3.2.1        dplyr_1.1.4           
[37] R.oo_1.26.0            RCurl_1.98-1.14       
[39] magrittr_2.0.3         GenomeInfoDbData_1.2.9
[41] interp_1.1-6           Matrix_1.6-5          
[43] Rcpp_1.0.12            fansi_1.0.6           
[45] lifecycle_1.0.4        R.methodsS3_1.8.2     
[47] stringi_1.8.3          yaml_2.3.8            
[49] MASS_7.3-56            zlibbioc_1.44.0       
[51] BiocFileCache_2.6.1    grid_4.2.0            
[53] blob_1.2.4             parallel_4.2.0        
[55] crayon_1.5.2           deldir_2.0-4          
[57] lattice_0.20-45        GenomicFeatures_1.50.4
[59] hms_1.1.3              KEGGREST_1.38.0       
[61] locfit_1.5-9.9         knitr_1.45            
[63] pillar_1.9.0           rjson_0.2.21          
[65] codetools_0.2-18       biomaRt_2.54.1        
[67] XML_3.99-0.16.1        glue_1.7.0            
[69] evaluate_0.23          latticeExtra_0.6-30   
[71] png_0.1-8              vctrs_0.6.5           
[73] cachem_1.0.8           xfun_0.42             
[75] aroma.light_3.28.0     restfulr_0.0.15       
[77] tibble_3.2.1           AnnotationDbi_1.60.2  
[79] memoise_2.0.1

You seem to be assuming that you can simply add up the normalizedCounts values from betweenLaneNormalization to get normalized library sizes. I am not familiar with EDASeq, but I suspect that assumption might not be correct. The normalizedCounts values might perhaps be counts-per-million, in which case the column sums wouldn't have any meaning. Unfortunately, the help page for betweenLaneNormalization doesn't give any information about the output object. According to Section 6.1 of the EDASeq vignette, you can extract log library sizes using the offset function.