I used Salmon for quasi-alignment-based quantification of my RNAseq data (mouse, mm10, raw fastq-files). I imported the generated quant.sf data files into R for use with edgeR, following the manual here: https://bioconductor.org/packages/devel/bioc/vignettes/tximport/inst/doc/tximport.html

Here is my code: 

# Importing the files with tximport

txi.salmon <- tximport(files, type = "salmon", tx2gene = tx2gene)

head(txi.salmon$counts)


#edgeR import
cts <- txi.salmon$counts
rownames(cts) <- gsub(rownames(cts), pattern = "\\.[0-9]+$", replacement = "") # Remove transcript version .**
normMat <- txi.salmon$length
normMat <- normMat/exp(rowMeans(log(normMat)))
o <- log(calcNormFactors(cts/normMat)) + log(colSums(cts/normMat))
y <- DGEList(cts)
y$offset <- t(t(log(normMat)) + o)
# y is now ready for estimate dispersion functions see edgeR User's Guide

My question is now whether I can proceed with the generated y object as I would usually do, namely in the following way:

# Pre-filtering edgeR

y$samples
keep <- rowSums(cpm(y) > 1) >= 2 # Outfilter low-count/unexpressed genes (more than 1 CPM in at least 2 libraries; smallest group size is 3)
summary(keep)
y <- y[keep, keep.lib.sizes=FALSE] # Re-calculate library sizes after count reductions
y <- calcNormFactors(y, method = "TMM") # Calculate the library normalization factors

y <- estimateDisp(y, design, robust = TRUE)
fit <- glmQLFit(y, design, robust = TRUE)

Is it correct to filter in this way and calculate the library normalization factors as I do here? Does edgeR "know" to use the y$offset information that was put in?

I am unsure as calcNormFactors was already called in the tximport part before. 

sessionInfo()
R version 3.4.2 (2017-09-28)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS/LAPACK: /scicore/soft/apps/OpenBLAS/0.2.13-GCC-4.8.4-LAPACK-3.5.0/lib/libopenblas_prescottp-r0.2.13.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8       
 [4] LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C              
[10] LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

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

other attached packages:
 [1] RSQLite_2.0            STRINGdb_1.18.0        rafalib_1.0.0          edgeR_3.20.9          
 [5] limma_3.34.9           biomaRt_2.34.2         tximport_1.6.0         GenomicFeatures_1.30.0
 [9] AnnotationDbi_1.40.0   Biobase_2.38.0         GenomicRanges_1.30.0   GenomeInfoDb_1.14.0   
[13] IRanges_2.12.0         S4Vectors_0.16.0       BiocGenerics_0.24.0    tximportData_1.6.0    

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.13               locfit_1.5-9.1             lattice_0.20-35           
 [4] prettyunits_1.0.2          png_0.1-7                  Rsamtools_1.30.0          
 [7] Biostrings_2.46.0          gtools_3.5.0               assertthat_0.2.0          
[10] digest_0.6.12              chron_2.3-51               R6_2.2.2                  
[13] plyr_1.8.4                 sqldf_0.4-11               httr_1.3.1                
[16] gplots_3.0.1               zlibbioc_1.24.0            rlang_0.1.4               
[19] progress_1.1.2             curl_3.0                   gdata_2.18.0              
[22] blob_1.1.0                 Matrix_1.2-12              hash_2.2.6                
[25] gsubfn_0.7                 proto_1.0.0                splines_3.4.2             
[28] RMySQL_0.10.13             BiocParallel_1.12.0        statmod_1.4.30            
[31] readr_1.1.1                stringr_1.2.0              igraph_1.1.2              
[34] RCurl_1.95-4.8             bit_1.1-12                 DelayedArray_0.4.1        
[37] compiler_3.4.2             rtracklayer_1.38.0         pkgconfig_2.0.1           
[40] tcltk_3.4.2                SummarizedExperiment_1.8.0 tibble_1.3.4              
[43] GenomeInfoDbData_0.99.1    matrixStats_0.52.2         XML_3.98-1.9              
[46] GenomicAlignments_1.14.1   bitops_1.0-6               grid_3.4.2                
[49] DBI_0.7                    magrittr_1.5               KernSmooth_2.23-15        
[52] stringi_1.1.6              XVector_0.18.0             rjson_0.2.15              
[55] RColorBrewer_1.1-2         tools_3.4.2                bit64_0.9-7               
[58] hms_0.4.2                  plotrix_3.6-6              yaml_2.1.14               
[61] caTools_1.17.1             memoise_1.1.0  

I don't use tximport so I can't speak for that particular section of your code, but otherwise, relevant edgeR functions will automatically detect and use whatever is present in the $offset field. However, we advise using scaleOffset when assigning an offset matrix to a DGEList. This simply adjusts the mean offset per gene so that the offsets are interpretable on the same scale as the log-library sizes, which ensures that the log-fold changes from glmQLFTest are not distorted.

It is also fine to re-compute the normalization factors here. If an offset matrix is available in the DGEList, the normalization factors will not be used to compute the offsets for GLM fitting, so there is no chance of "double normalization".

It shouldn't matter if you run calcNormFactors. There is a function in edgeR called getOffset:

> getOffset
function (y)
{
    offset <- y$offset
    lib.size <- y$samples$lib.size
    norm.factors <- y$samples$norm.factors
    if (!is.null(offset)) {
        return(offset)
    }
    else {
        if (!is.null(norm.factors))
            lib.size <- lib.size * norm.factors
        return(log(lib.size))
    }
}
<bytecode: 0x00000000201ae308>
<environment: namespace:edgeR>

And as you can see, if there is an offset matrix already, that will be used. Otherwise an offset will be computed using the library size and normalization factors.