Hi all. I´m doing DEG analysis using kallisto and DESeq2. I was able to complete the analysis and identify differentially expressed transcripts. However, I noticed I could not extract size factors from the DESeq2 object

I imported the estimated counts from kallisto tsv files as such:

kallisto_Counts <- tximport(files, type="kallisto", txOut = TRUE, dropInfReps = TRUE)
ddskallisto <- DESeqDataSetFromTximport(kallisto_Counts, colData = sampleTable, design = ~ condition)

And computed the sizes factors using the DESeq function

dds <- DESeq(ddskallisto)


estimating size factors
using 'avgTxLength' from assays(dds), correcting for library size
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing

I also tried to compute using the Size factor with the dedicated function

dds_with_sz <- estimateSizeFactors(dds) 
#using 'avgTxLength' from assays(dds), correcting for library size

In both cases when I tried to access the size factor it returned NULL

sizeFactors(dds)
#NULL

I´m unsure if the results are nonsensical because the counts may have not been normalized for sequencing deepness (at least that is my understanding of the size factor operation). Using the counts and the assay function return different count matrice, but I´m still unsure if I´m interpreting this right.

Thanks in advance

> sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18362)

Matrix products: default

locale:
[1] LC_COLLATE=English_United Kingdom.1252 
[2] LC_CTYPE=English_United Kingdom.1252   
[3] LC_MONETARY=English_United Kingdom.1252
[4] LC_NUMERIC=C                           
[5] LC_TIME=English_United Kingdom.1252    

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

other attached packages:
 [1] tximport_1.14.2             ggrepel_0.8.2              
 [3] pheatmap_1.0.12             dplyr_0.8.5                
 [5] RColorBrewer_1.1-2          ggplot2_3.3.0              
 [7] DESeq2_1.26.0               SummarizedExperiment_1.16.1
 [9] DelayedArray_0.12.3         BiocParallel_1.20.1        
[11] matrixStats_0.56.0          Biobase_2.46.0             
[13] GenomicRanges_1.38.0        GenomeInfoDb_1.22.1        
[15] IRanges_2.20.2              S4Vectors_0.24.4           
[17] BiocGenerics_0.32.0        

loaded via a namespace (and not attached):
 [1] bit64_0.9-7            splines_3.6.1          Formula_1.2-3         
 [4] assertthat_0.2.1       latticeExtra_0.6-29    blob_1.2.1            
 [7] GenomeInfoDbData_1.2.2 pillar_1.4.3           RSQLite_2.2.0         
[10] backports_1.1.6        lattice_0.20-38        glue_1.4.0            
[13] digest_0.6.25          XVector_0.26.0         checkmate_2.0.0       
[16] colorspace_1.4-1       htmltools_0.4.0        Matrix_1.2-17         
[19] XML_3.99-0.3           pkgconfig_2.0.3        genefilter_1.68.0     
[22] zlibbioc_1.32.0        purrr_0.3.4            xtable_1.8-4          
[25] scales_1.1.0           jpeg_0.1-8.1           htmlTable_1.13.3      
[28] tibble_3.0.1           annotate_1.64.0        ellipsis_0.3.0        
[31] withr_2.2.0            nnet_7.3-12            survival_3.1-12       
[34] magrittr_1.5           crayon_1.3.4           memoise_1.1.0         
[37] foreign_0.8-71         tools_3.6.1            data.table_1.12.8     
[40] hms_0.5.3              lifecycle_0.2.0        stringr_1.4.0         
[43] locfit_1.5-9.4         munsell_0.5.0          cluster_2.1.0         
[46] AnnotationDbi_1.48.0   compiler_3.6.1         rlang_0.4.5           
[49] grid_3.6.1             RCurl_1.98-1.1         rstudioapi_0.11       
[52] htmlwidgets_1.5.1      bitops_1.0-6           base64enc_0.1-3       
[55] gtable_0.3.0           DBI_1.1.0              R6_2.4.1              
[58] gridExtra_2.3          knitr_1.28             bit_1.1-15.2          
[61] Hmisc_4.4-0            readr_1.3.1            stringi_1.4.6         
[64] Rcpp_1.0.4.6           geneplotter_1.64.0     vctrs_0.2.4           
[67] rpart_4.1-15           acepack_1.4.1          png_0.1-7             
[70] tidyselect_1.0.0       xfun_0.13

When average transcript lengths are used as offsets (in the tximeta / tximport pipeline this is the case), then we don't have sj (in the 2014 paper notation, see vignette section on the DESeq2 model), but we have sij which are gene- and sample-specific normalization factors. You can access these with:

normalizationFactors(dds)

These aren't the same across genes. If you want to get a sense for the sequencing depth trend you could take the geometric or arithmetic mean of the columns.