Hello,

I am trying to compare gene expression of a set of genes between pancreatic cancer (TCGA-PAAD) vs normal pancreas (GTEx) data at normalized count level or through differential expression analysis (edgeR, or DESeq2). I have managed to use TCGAquery_recount2() from TCGABiolinks (code below) to obtain the count data from both studies. Are these the raw count data? If so, can I simply merge the matrices and then perform scaling/normalizing for my subsequent analysis?

Any advice would be greatly appreciated!

pancreas.tcga <- TCGAquery_recount2(project = "tcga", tissue = "pancreas")
pancreas.gtex <- TCGAquery_recount2(project = "gtex", tissue = "pancreas")

count.tcga <- assay(pancreas.tcga$tcga_pancreas)
count.gtex <- assay(pancreas.gtex$gtex_pancreas)

sessionInfo()
R version 4.0.4 (2021-02-15)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19043)

Matrix products: default

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

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

other attached packages:
 [1] genefilter_1.72.1           gProfileR_0.7.0             RColorBrewer_1.1-2          survminer_0.4.9            
 [5] ggpubr_0.4.0                survival_3.2-7              gplots_3.1.1                SummarizedExperiment_1.20.0
 [9] Biobase_2.50.0              GenomicRanges_1.42.0        GenomeInfoDb_1.26.7         IRanges_2.24.1             
[13] S4Vectors_0.28.1            BiocGenerics_0.36.1         MatrixGenerics_1.2.1        matrixStats_0.61.0         
[17] caret_6.0-90                lattice_0.20-41             FactoMineR_2.4              factoextra_1.0.7           
[21] ggplot2_3.3.5               glmnet_4.1-3                Matrix_1.3-2                edgeR_3.32.1               
[25] limma_3.46.0                TCGAbiolinks_2.18.0        

loaded via a namespace (and not attached):
  [1] backports_1.3.0             BiocFileCache_1.14.0        plyr_1.8.6                  splines_4.0.4              
  [5] listenv_0.8.0               digest_0.6.28               foreach_1.5.1               htmltools_0.5.2            
  [9] fansi_0.5.0                 magrittr_2.0.1              memoise_2.0.0               cluster_2.1.0              
 [13] tzdb_0.2.0                  annotate_1.68.0             recipes_0.1.17              globals_0.14.0             
 [17] readr_2.0.2                 gower_0.2.2                 R.utils_2.11.0              askpass_1.1                
 [21] prettyunits_1.1.1           colorspace_2.0-2            blob_1.2.2                  rvest_1.0.2                
 [25] rappdirs_0.3.3              ggrepel_0.9.1               xfun_0.26                   dplyr_1.0.7                
 [29] crayon_1.4.2                RCurl_1.98-1.5              jsonlite_1.7.2              zoo_1.8-9                  
 [33] iterators_1.0.13            glue_1.4.2                  gtable_0.3.0                ipred_0.9-12               
 [37] zlibbioc_1.36.0             XVector_0.30.0              DelayedArray_0.16.3         car_3.0-12                 
 [41] future.apply_1.8.1          shape_1.4.6                 abind_1.4-5                 scales_1.1.1               
 [45] DBI_1.1.1                   rstatix_0.7.0               Rcpp_1.0.7                  xtable_1.8-4               
 [49] progress_1.2.2              flashClust_1.01-2           bit_4.0.4                   km.ci_0.5-2                
 [53] lava_1.6.10                 prodlim_2019.11.13          DT_0.19                     htmlwidgets_1.5.4          
 [57] httr_1.4.2                  ellipsis_0.3.2              pkgconfig_2.0.3             XML_3.99-0.8               
 [61] R.methodsS3_1.8.1           nnet_7.3-15                 dbplyr_2.1.1                locfit_1.5-9.4             
 [65] utf8_1.2.2                  tidyselect_1.1.1            rlang_0.4.12                reshape2_1.4.4             
 [69] AnnotationDbi_1.52.0        munsell_0.5.0               tools_4.0.4                 cachem_1.0.6               
 [73] downloader_0.4              generics_0.1.1              RSQLite_2.2.8               broom_0.7.10               
 [77] stringr_1.4.0               fastmap_1.1.0               ModelMetrics_1.2.2.2        knitr_1.36                 
 [81] bit64_4.0.5                 survMisc_0.5.5              caTools_1.18.2              purrr_0.3.4                
 [85] future_1.23.0               nlme_3.1-152                R.oo_1.24.0                 leaps_3.1                  
 [89] xml2_1.3.2                  biomaRt_2.46.3              compiler_4.0.4              curl_4.3.2                 
 [93] ggsignif_0.6.3              tibble_3.1.5                stringi_1.7.5               TCGAbiolinksGUI.data_1.10.0
 [97] KMsurv_0.1-5                vctrs_0.3.8                 pillar_1.6.4                lifecycle_1.0.1            
[101] BiocManager_1.30.16         data.table_1.14.2           bitops_1.0-7                R6_2.5.1                   
[105] gridExtra_2.3               KernSmooth_2.23-18          parallelly_1.28.1           codetools_0.2-18           
[109] MASS_7.3-53                 gtools_3.9.2                assertthat_0.2.1            openssl_1.4.5              
[113] withr_2.4.2                 GenomeInfoDbData_1.2.4      hms_1.1.1                   grid_4.0.4                 
[117] rpart_4.1-15                timeDate_3043.102           tidyr_1.1.4                 class_7.3-18               
[121] carData_3.0-4               pROC_1.18.0                 scatterplot3d_0.3-41        lubridate_1.8.0            
[125] tinytex_0.35