After obtaining the gene-level expression for the CCLE project using recount3, I am trying to obtain the transcript-level data.

As far as I can tell this is not possible with recount3 but it should be possible with recount:

library("recount")
download_study("SRP009615", type = "rse-exon")
load(file.path("SRP009615", "rse_exon.Rdata"))
unlink("SRP009615", recursive = TRUE)
head(assay(rse_exon))

however CCLE doens't seem to have been processed in recount

download_study("SRP186687", type = "rse-tx")
Error in download_study("SRP186687", type = "rse-tx") : 
  Invalid 'project' argument. There's no such 'project' in the recount_url data.frame.


project_info <- abstract_search("SRP186687")
[1] number_samples species        abstract       project       
<0 rows> (or 0-length row.names)

project_info <- abstract_search("CCLE")
[1] number_samples species        abstract       project       
<0 rows> (or 0-length row.names)

So my question is:

How can the transcript level CCLE data be obtained either with recount, recount3 or any other way?

Cheers

sessionInfo( )
R version 4.3.2 (2023-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.6 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0 
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0

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

time zone: Europe/Berlin
tzcode source: system (glibc)

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

other attached packages:
 [1] recount_1.28.0              recount3_1.12.0             SummarizedExperiment_1.32.0 Biobase_2.62.0              GenomicRanges_1.54.1        GenomeInfoDb_1.38.5        
 [7] IRanges_2.36.0              S4Vectors_0.40.2            BiocGenerics_0.48.1         MatrixGenerics_1.14.0       matrixStats_1.2.0           magrittr_2.0.3             
[13] data.table_1.14.10         

loaded via a namespace (and not attached):
  [1] jsonlite_1.8.8           rstudioapi_0.15.0        GenomicFeatures_1.54.1   rmarkdown_2.25           BiocIO_1.12.0            zlibbioc_1.48.0          vctrs_0.6.5             
  [8] memoise_2.0.1            Rsamtools_2.18.0         RCurl_1.98-1.14          base64enc_0.1-3          htmltools_0.5.7          S4Arrays_1.2.0           progress_1.2.3          
 [15] curl_5.2.0               SparseArray_1.2.3        Formula_1.2-5            htmlwidgets_1.6.4        plyr_1.8.9               cachem_1.0.8             GenomicAlignments_1.38.2
 [22] iterators_1.0.14         lifecycle_1.0.4          pkgconfig_2.0.3          Matrix_1.6-5             R6_2.5.1                 fastmap_1.1.1            GenomeInfoDbData_1.2.11 
 [29] digest_0.6.34            colorspace_2.1-0         AnnotationDbi_1.64.1     Hmisc_5.1-1              RSQLite_2.3.5            filelock_1.0.3           fansi_1.0.6             
 [36] httr_1.4.7               abind_1.4-5              compiler_4.3.2           rngtools_1.5.2           downloader_0.4           bit64_4.0.5              htmlTable_2.4.2         
 [43] backports_1.4.1          BiocParallel_1.36.0      DBI_1.2.1                R.utils_2.12.3           biomaRt_2.58.0           rappdirs_0.3.3           DelayedArray_0.28.0     
 [50] sessioninfo_1.2.2        rjson_0.2.21             derfinderHelper_1.36.0   tools_4.3.2              foreign_0.8-85           rentrez_1.2.3            nnet_7.3-19             
 [57] R.oo_1.25.0              glue_1.7.0               restfulr_0.0.15          grid_4.3.2               checkmate_2.3.1          reshape2_1.4.4           derfinder_1.36.0        
 [64] cluster_2.1.4            generics_0.1.3           gtable_0.3.4             BSgenome_1.70.1          tzdb_0.4.0               R.methodsS3_1.8.2        tidyr_1.3.0             
 [71] hms_1.1.3                xml2_1.3.6               utf8_1.2.4               XVector_0.42.0           foreach_1.5.2            pillar_1.9.0             stringr_1.5.1           
 [78] limma_3.58.1             splines_4.3.2            dplyr_1.1.4              BiocFileCache_2.10.1     lattice_0.22-5           rtracklayer_1.62.0       bit_4.0.5               
 [85] GEOquery_2.70.0          tidyselect_1.2.0         locfit_1.5-9.8           Biostrings_2.70.1        knitr_1.45               gridExtra_2.3            xfun_0.41               
 [92] statmod_1.5.0            stringi_1.8.3            yaml_2.3.8               evaluate_0.23            codetools_0.2-19         GenomicFiles_1.38.0      qvalue_2.34.0           
 [99] tibble_3.2.1             cli_3.6.2                bumphunter_1.44.0        rpart_4.1.21             munsell_0.5.0            Rcpp_1.0.12              dbplyr_2.4.0            
[106] png_0.1-8                XML_3.99-0.16.1          parallel_4.3.2           ggplot2_3.4.4            readr_2.1.5              blob_1.2.4               prettyunits_1.2.0       
[113] doRNG_1.8.6              bitops_1.0-7             VariantAnnotation_1.48.1 scales_1.3.0             purrr_1.0.2              crayon_1.5.2             rlang_1.1.3             
[120] KEGGREST_1.42.0

Hi,

Thank you for your interest in both the recount2 (accessible via the recount package; yes, I know it's confusing) and recount3 projects. For the recount2 project, we have a pre-print led by Jack Fu described at https://doi.org/10.1101/247346. Transcript-level estimated counts are available for the studies that were processed as part of the recount2 project. Either via the shiny web application for the project or via the recount package.

For the recount3 project, while we did run Salmon as part of Monorail, we only did so for 1 annotation and did not make those results public. So you cannot access them through recount3 or elsewhere.

Best, Leo