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If you have two mass_dataset, you can use cbind or rbind to merge them as one mass_dataset.

cbind()

cbind() means that two mass_dataset have same variables and totally different samples.

library(massdataset)
library(tidyverse)

data("expression_data")
data("sample_info")
data("sample_info_note")
data("variable_info")
data("variable_info_note")

object =
  create_mass_dataset(
    expression_data = expression_data,
    sample_info = sample_info,
    variable_info = variable_info,
    sample_info_note = sample_info_note,
    variable_info_note = variable_info_note
  )
  
object
#> -------------------- 
#> massdataset version: 0.99.1 
#> -------------------- 
#> 1.expression_data:[ 1000 x 8 data.frame]
#> 2.sample_info:[ 8 x 4 data.frame]
#> 8 samples:Blank_3 Blank_4 QC_1 ... PS4P3 PS4P4
#> 3.variable_info:[ 1000 x 3 data.frame]
#> 1000 variables:M136T55_2_POS M79T35_POS M307T548_POS ... M232T937_POS M301T277_POS
#> 4.sample_info_note:[ 4 x 2 data.frame]
#> 5.variable_info_note:[ 3 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> -------------------- 
#> Processing information
#> 1 processings in total
#> create_mass_dataset ---------- 
#>       Package         Function.used                Time
#> 1 massdataset create_mass_dataset() 2026-03-02 09:27:44
object1 = 
  object[,1:4]

object2 = 
  object[,5:8]

colnames(object1)
#> [1] "Blank_3" "Blank_4" "QC_1"    "QC_2"
colnames(object2)
#> [1] "PS4P1" "PS4P2" "PS4P3" "PS4P4"
cbind(object1, object2)
#> -------------------- 
#> massdataset version: 0.99.1 
#> -------------------- 
#> 1.expression_data:[ 1000 x 8 data.frame]
#> 2.sample_info:[ 8 x 4 data.frame]
#> 8 samples:Blank_3 Blank_4 QC_1 ... PS4P3 PS4P4
#> 3.variable_info:[ 1000 x 3 data.frame]
#> 1000 variables:M136T55_2_POS M79T35_POS M307T548_POS ... M232T937_POS M301T277_POS
#> 4.sample_info_note:[ 4 x 2 data.frame]
#> 5.variable_info_note:[ 3 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> -------------------- 
#> Processing information
#> 4 processings in total
#> create_mass_dataset ---------- 
#>       Package         Function.used                Time
#> 1 massdataset create_mass_dataset() 2026-03-02 09:27:44
#> subset ---------- 
#>       Package Function.used                Time
#> 1 massdataset             [ 2026-03-02 09:27:44
#> create_mass_dataset ---------- 
#>       Package         Function.used                Time
#> 1 massdataset create_mass_dataset() 2026-03-02 09:27:44
#> subset ---------- 
#>       Package Function.used                Time
#> 1 massdataset             [ 2026-03-02 09:27:44

rbind()

rbind() means that two mass_dataset have same samples and totally different variables

object1 = 
  object[1:10,]

object2 = 
  object[11:15,]

rownames(object1)
#>  [1] "M136T55_2_POS" "M79T35_POS"    "M307T548_POS"  "M183T224_POS" 
#>  [5] "M349T47_POS"   "M182T828_POS"  "M299T359_POS"  "M348T844_POS" 
#>  [9] "M344T471_POS"  "M181T436_POS"
rownames(object2)
#> [1] "M345T195_POS" "M304T825_POS" "M137T196_POS" "M359T638_POS" "M270T507_POS"
rbind(object1, object2)
#> -------------------- 
#> massdataset version: 0.99.1 
#> -------------------- 
#> 1.expression_data:[ 15 x 8 data.frame]
#> 2.sample_info:[ 8 x 4 data.frame]
#> 8 samples:Blank_3 Blank_4 QC_1 ... PS4P3 PS4P4
#> 3.variable_info:[ 15 x 3 data.frame]
#> 15 variables:M136T55_2_POS M79T35_POS M307T548_POS ... M359T638_POS M270T507_POS
#> 4.sample_info_note:[ 4 x 2 data.frame]
#> 5.variable_info_note:[ 3 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> -------------------- 
#> Processing information
#> 4 processings in total
#> create_mass_dataset ---------- 
#>       Package         Function.used                Time
#> 1 massdataset create_mass_dataset() 2026-03-02 09:27:44
#> subset ---------- 
#>       Package Function.used                Time
#> 1 massdataset             [ 2026-03-02 09:27:44
#> create_mass_dataset ---------- 
#>       Package         Function.used                Time
#> 1 massdataset create_mass_dataset() 2026-03-02 09:27:44
#> subset ---------- 
#>       Package Function.used                Time
#> 1 massdataset             [ 2026-03-02 09:27:44

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