Split `mass_dataset`

In massdataset package, the split_mass_dataset is used to split mass_dataset to different class objects.

Data preparation

library(massdataset)
library(tidyverse)

data("expression_data")
data("sample_info")
data("variable_info")

object =
  create_mass_dataset(
    expression_data = expression_data,
    sample_info = sample_info,
    variable_info = variable_info
  )

Split based on sample information

object <-
  activate_mass_dataset(object, what = "sample_info")

new_object <-
  split_mass_dataset(object = object, by = "group")

new_object %>% lapply(dim)
#> $Blank
#> variables   samples 
#>      1000         2 
#> 
#> $QC
#> variables   samples 
#>      1000         2 
#> 
#> $Subject
#> variables   samples 
#>      1000         4
new_object %>% lapply(colnames)
#> $Blank
#> [1] "Blank_3" "Blank_4"
#> 
#> $QC
#> [1] "QC_1" "QC_2"
#> 
#> $Subject
#> [1] "PS4P1" "PS4P2" "PS4P3" "PS4P4"

extract_process_info(new_object[[1]])$split_mass_dataset
#> -------------------- 
#> pacakge_name: massdataset 
#> function_name: split_mass_dataset 
#> time: 2026-03-02 09:28:28.555711 
#> parameters:
#> by : group 
#> fun : no

Split based on variable information

```{r,eval=TRUE,warning=FALSE, R.options=““, message=FALSE, cache=FALSE, fig.alt=c(”Retention time distribution for the first split mass_dataset object.”, “Retention time distribution for the second split mass_dataset object.”)} object <- activate_mass_dataset(object, what = “variable_info”)

new_object <- split_mass_dataset(object = object, by = “rt”, fun = function(rt) rt > 600)

new_object %>% lapply(dim)

plot(extract_variable_info(new_object[[1]]) $rt) plot(extract_variable_info(new_object[[2]])$ rt)



``` r
extract_process_info(new_object[[1]])$split_mass_dataset
#>  [33m-------------------- 
#>  [39m [32mpacakge_name: massdataset [39m 
#>  [32mfunction_name: split_mass_dataset [39m 
#>  [32mtime: 2026-03-02 09:28:28.555711 [39m 
#>  [32mparameters:
#>  [39m [32mby : group [39m 
#>  [32mfun : no [39m

Session information

sessionInfo()
#> R version 4.5.2 (2025-10-31)
#> Platform: aarch64-apple-darwin20
#> Running under: macOS Tahoe 26.3
#> 
#> Matrix products: default
#> BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib 
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1
#> 
#> locale:
#> [1] C.UTF-8/C.UTF-8/C.UTF-8/C/C.UTF-8/C.UTF-8
#> 
#> time zone: Asia/Singapore
#> tzcode source: internal
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#>  [1] lubridate_1.9.4    forcats_1.0.0      stringr_1.5.1      purrr_1.1.0       
#>  [5] readr_2.1.5        tidyr_1.3.1        tibble_3.3.0       tidyverse_2.0.0   
#>  [9] magrittr_2.0.3     dplyr_1.1.4        ggplot2_4.0.2      massdataset_0.99.1
#> 
#> loaded via a namespace (and not attached):
#>  [1] tidyselect_1.2.1            farver_2.1.2               
#>  [3] S7_0.2.0                    fastmap_1.2.0              
#>  [5] digest_0.6.37               timechange_0.3.0           
#>  [7] lifecycle_1.0.4             cluster_2.1.8.1            
#>  [9] compiler_4.5.2              rlang_1.1.6                
#> [11] sass_0.4.10                 tools_4.5.2                
#> [13] yaml_2.3.10                 knitr_1.50                 
#> [15] S4Arrays_1.8.1              htmlwidgets_1.6.4          
#> [17] DelayedArray_0.34.1         RColorBrewer_1.1-3         
#> [19] abind_1.4-8                 withr_3.0.2                
#> [21] BiocGenerics_0.54.0         desc_1.4.3                 
#> [23] grid_4.5.2                  stats4_4.5.2               
#> [25] colorspace_2.1-1            scales_1.4.0               
#> [27] iterators_1.0.14            dichromat_2.0-0.1          
#> [29] SummarizedExperiment_1.38.1 cli_3.6.5                  
#> [31] rmarkdown_2.29              crayon_1.5.3               
#> [33] ragg_1.4.0                  generics_0.1.4             
#> [35] rstudioapi_0.17.1           httr_1.4.7                 
#> [37] tzdb_0.5.0                  rjson_0.2.23               
#> [39] cachem_1.1.0                parallel_4.5.2             
#> [41] XVector_0.48.0              matrixStats_1.5.0          
#> [43] vctrs_0.6.5                 Matrix_1.7-4               
#> [45] jsonlite_2.0.0              IRanges_2.42.0             
#> [47] hms_1.1.3                   GetoptLong_1.0.5           
#> [49] S4Vectors_0.48.0            clue_0.3-66                
#> [51] systemfonts_1.2.3           foreach_1.5.2              
#> [53] jquerylib_0.1.4             glue_1.8.0                 
#> [55] pkgdown_2.1.3               codetools_0.2-20           
#> [57] stringi_1.8.7               shape_1.4.6.1              
#> [59] gtable_0.3.6                GenomeInfoDb_1.44.2        
#> [61] GenomicRanges_1.60.0        UCSC.utils_1.4.0           
#> [63] ComplexHeatmap_2.24.1       pillar_1.11.0              
#> [65] htmltools_0.5.8.1           GenomeInfoDbData_1.2.14    
#> [67] circlize_0.4.16             R6_2.6.1                   
#> [69] textshaping_1.0.1           doParallel_1.0.17          
#> [71] evaluate_1.0.4              Biobase_2.68.0             
#> [73] lattice_0.22-7              png_0.1-8                  
#> [75] openxlsx_4.2.8              bslib_0.9.0                
#> [77] Rcpp_1.1.0                  zip_2.3.3                  
#> [79] SparseArray_1.8.1           xfun_0.53                  
#> [81] fs_1.6.6                    MatrixGenerics_1.20.0      
#> [83] pkgconfig_2.0.3             GlobalOptions_0.1.2

Xiaotao Shen

Created on 2022-03-05 and updated on 2026-03-02

Data preparation

Split based on sample information

Split based on variable information

Session information