Hi all, In this summarizedexperiment I can't figure out how to iterate over entries which are at the 'deepest' (not sure of the correct terminology but requiring the most brackets) level. My crude attempt at a for loop where treatments is a list of dataframes (121 indices long) and the 7th column within each dataframe ("treatment_type") is what I want to print/save from each dataframe,

for(i in dds@colData@listData[["treatments"]][[1:121]][["treatment_type"]]){
  print(i)
}

gets a recursive indexing failed at level 3 error. So now I'm thinking if I have to independently save each element of the "treatments" list first (where each element is it's own dataframe) it quickly becomes manually demanding which is the opposite of what scripting is supposed to be about. But I haven't found anything close searching through the forum.

> sessionInfo()
R version 4.2.1 (2022-06-23)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.4 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_AU.UTF-8       LC_NUMERIC=C               LC_TIME=en_AU.UTF-8        LC_COLLATE=en_AU.UTF-8     LC_MONETARY=en_AU.UTF-8    LC_MESSAGES=en_AU.UTF-8   
 [7] LC_PAPER=en_AU.UTF-8       LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C       

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

other attached packages:
 [1] org.Hs.eg.db_3.15.0            AnnotationDbi_1.58.0           EnhancedVolcano_1.14.0         ggrepel_0.9.1                  ggplot2_3.3.6                 
 [6] BiocParallel_1.30.3            DESeq2_1.36.0                  pipette_0.8.0                  tidySummarizedExperiment_1.6.1 tidybulk_1.8.0                
[11] dplyr_1.0.9                    SummarizedExperiment_1.26.1    Biobase_2.56.0                 GenomicRanges_1.48.0           GenomeInfoDb_1.32.2           
[16] IRanges_2.30.0                 S4Vectors_0.34.0               BiocGenerics_0.42.0            MatrixGenerics_1.8.0           matrixStats_0.62.0            

loaded via a namespace (and not attached):
  [1] colorspace_2.0-3       ellipsis_0.3.2         XVector_0.36.0         rstudioapi_0.13        farver_2.1.0           bit64_4.0.5            mvtnorm_1.1-3         
  [8] RSpectra_0.16-1        fansi_1.0.3            apeglm_1.18.0          codetools_0.2-18       splines_4.2.1          cachem_1.0.6           goalie_0.6.0          
 [15] geneplotter_1.74.0     jsonlite_1.8.0         umap_0.2.8.0           annotate_1.74.0        ashr_2.2-54            png_0.1-7              readr_2.1.2           
 [22] compiler_4.2.1         httr_1.4.3             assertthat_0.2.1       Matrix_1.4-1           fastmap_1.1.0          lazyeval_0.2.2         cli_3.3.0             
 [29] htmltools_0.5.2        tools_4.2.1            coda_0.19-4            gtable_0.3.0           glue_1.6.2             GenomeInfoDbData_1.2.8 Rcpp_1.0.8.3          
 [36] bbmle_1.0.25           vctrs_0.4.1            Biostrings_2.64.0      AcidGenerics_0.6.0     preprocessCore_1.58.0  stringr_1.4.0          ps_1.7.1              
 [43] syntactic_0.5.2        lifecycle_1.0.1        irlba_2.3.5            XML_3.99-0.10          AcidCLI_0.2.0          zlibbioc_1.42.0        MASS_7.3-57           
 [50] scales_1.2.0           hms_1.1.1              parallel_4.2.1         RColorBrewer_1.1-3     memoise_2.0.1          reticulate_1.25        emdbook_1.3.12        
 [57] bdsmatrix_1.3-6        stringi_1.7.6          RSQLite_2.2.14         SQUAREM_2021.1         genefilter_1.78.0      BiocIO_1.6.0           truncnorm_1.0-8       
 [64] rlang_1.0.2            pkgconfig_2.0.3        bitops_1.0-7           lattice_0.20-45        invgamma_1.1           purrr_0.3.4            htmlwidgets_1.5.4     
 [71] labeling_0.4.2         bit_4.0.4              processx_3.6.1         tidyselect_1.1.2       plyr_1.8.7             magrittr_2.0.3         R6_2.5.1              
 [78] generics_0.1.2         DelayedArray_0.22.0    DBI_1.1.3              pillar_1.7.0           withr_2.5.0            survival_3.3-1         KEGGREST_1.36.2       
 [85] RCurl_1.98-1.7         mixsqp_0.3-43          tibble_3.1.7           crayon_1.5.1           utf8_1.2.2             plotly_4.10.0          tzdb_0.3.0            
 [92] locfit_1.5-9.5         grid_4.2.1             data.table_1.14.2      blob_1.2.3             digest_0.6.29          xtable_1.8-4           AcidBase_0.5.0        
 [99] numDeriv_2016.8-1.1    tidyr_1.2.0            openssl_2.0.2          munsell_0.5.0          viridisLite_0.4.0      askpass_1.1

In general it is bad practice to manually access slots in an object via @. There are dedicated getter and setter functions for this. Here that is colData(dds) to get the colData DataFrame. Can you give a reproducible example of what you want to do? I cannot say I get the question. In all likelihood, a loop is not what you need.

Thanks for your help, I have cleaned up some of my code by removing @, that was very handy to learn (side note, I think I got into the habit of using @ as the console displays @ based accessing after using View() on your data, oops).

Adding this in case it is useful to any beginners in the future. If you have a column of colData that is a list of dataframes and you want to access some element within each dataframe, a starting point for you might be as follows:

my_list <- colData(dds)$list_of_dataframes

for(i in 1:length(my_list){
  print(my_list[[i]]["name_of_column_of_interest_within_dataframe"])
}