Extract data from mass_dataset
Xiaotao Shen
Created on 2021-12-04 and updated on 2026-03-02
Source:vignettes/extract_data.Rmd
extract_data.Rmd
Use extract_xxx functions
We first created a mass_dataset class object.
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 09:27:54In massdataset package, there are a series of functions
named as extract_xxx(), users can use them to extract data
from mass_dataset calss object.
##sample_info
extract_sample_info(object)
#> sample_id injection.order class group
#> 1 Blank_3 1 Blank Blank
#> 2 Blank_4 2 Blank Blank
#> 3 QC_1 3 QC QC
#> 4 QC_2 4 QC QC
#> 5 PS4P1 5 Subject Subject
#> 6 PS4P2 6 Subject Subject
#> 7 PS4P3 7 Subject Subject
#> 8 PS4P4 8 Subject Subject
##variable_info
extract_variable_info(object) %>% head()
#> variable_id mz rt
#> 1 M136T55_2_POS 136.06140 54.97902
#> 2 M79T35_POS 79.05394 35.36550
#> 3 M307T548_POS 307.14035 547.56641
#> 4 M183T224_POS 183.06209 224.32777
#> 5 M349T47_POS 349.01584 47.00262
#> 6 M182T828_POS 181.99775 828.35712
##expression_data
extract_expression_data(object) %>% head()
#> Blank_3 Blank_4 QC_1 QC_2 PS4P1 PS4P2 PS4P3
#> M136T55_2_POS NA NA 1857924.8 1037763.8 1494436.1 3496912.1 1959179
#> M79T35_POS NA NA 2821550.2 1304875.3 2471336.1 3333582.7 2734244
#> M307T548_POS NA NA 410387.6 273687.8 288590.2 137297.5 NA
#> M183T224_POS NA NA NA NA NA 5059068.1 5147422
#> M349T47_POS NA NA 8730104.8 4105598.5 5141073.2 8424315.6 7896633
#> M182T828_POS 3761893 2572593 NA 3662819.1 5700534.8 4600172.4 5557015
#> PS4P4
#> M136T55_2_POS 1005418.8
#> M79T35_POS 3361452.3
#> M307T548_POS 271318.3
#> M183T224_POS NA
#> M349T47_POS 6441449.0
#> M182T828_POS 4433034.2
##sample_info_note
extract_sample_info_note(object)
#> name meaning
#> 1 sample_id sample_id
#> 2 injection.order injection.order
#> 3 class class
#> 4 group group
##variable_info_note
extract_variable_info_note(object)
#> name meaning
#> 1 variable_id variable_id
#> 2 mz mz
#> 3 rt rt
##ms2_data
extract_ms2_data(object)
#> list()
##process_info
extract_annotation_table(object)
#> data frame with 0 columns and 0 rows
##process_info
extract_process_info(object)
#> $create_mass_dataset
#> --------------------
#> pacakge_name: massdataset
#> function_name: create_mass_dataset()
#> time: 2026-03-02 09:27:54.550095
#> parameters:
#> no : noUse slot()
slot(object = object, name = "sample_info")
#> sample_id injection.order class group
#> 1 Blank_3 1 Blank Blank
#> 2 Blank_4 2 Blank Blank
#> 3 QC_1 3 QC QC
#> 4 QC_2 4 QC QC
#> 5 PS4P1 5 Subject Subject
#> 6 PS4P2 6 Subject Subject
#> 7 PS4P3 7 Subject Subject
#> 8 PS4P4 8 Subject Subject
slot(object = object, name = "variable_info") %>% head()
#> variable_id mz rt
#> 1 M136T55_2_POS 136.06140 54.97902
#> 2 M79T35_POS 79.05394 35.36550
#> 3 M307T548_POS 307.14035 547.56641
#> 4 M183T224_POS 183.06209 224.32777
#> 5 M349T47_POS 349.01584 47.00262
#> 6 M182T828_POS 181.99775 828.35712
slot(object = object, name = "expression_data") %>% head()
#> Blank_3 Blank_4 QC_1 QC_2 PS4P1 PS4P2 PS4P3
#> M136T55_2_POS NA NA 1857924.8 1037763.8 1494436.1 3496912.1 1959179
#> M79T35_POS NA NA 2821550.2 1304875.3 2471336.1 3333582.7 2734244
#> M307T548_POS NA NA 410387.6 273687.8 288590.2 137297.5 NA
#> M183T224_POS NA NA NA NA NA 5059068.1 5147422
#> M349T47_POS NA NA 8730104.8 4105598.5 5141073.2 8424315.6 7896633
#> M182T828_POS 3761893 2572593 NA 3662819.1 5700534.8 4600172.4 5557015
#> PS4P4
#> M136T55_2_POS 1005418.8
#> M79T35_POS 3361452.3
#> M307T548_POS 271318.3
#> M183T224_POS NA
#> M349T47_POS 6441449.0
#> M182T828_POS 4433034.2
slot(object = object, name = "sample_info_note")
#> name meaning
#> 1 sample_id sample_id
#> 2 injection.order injection.order
#> 3 class class
#> 4 group group
slot(object = object, name = "variable_info_note")
#> name meaning
#> 1 variable_id variable_id
#> 2 mz mz
#> 3 rt rt
slot(object = object, name = "ms2_data")
#> list()
slot(object = object, name = "process_info")
#> $create_mass_dataset
#> --------------------
#> pacakge_name: massdataset
#> function_name: create_mass_dataset()
#> time: 2026-03-02 09:27:54.550095
#> parameters:
#> no : no
slot(object = object, name = "annotation_table")
#> data frame with 0 columns and 0 rows
Use @
mass_data class is a S4 object. So we can also use
@.
object@expression_data %>% head()
#> Blank_3 Blank_4 QC_1 QC_2 PS4P1 PS4P2 PS4P3
#> M136T55_2_POS NA NA 1857924.8 1037763.8 1494436.1 3496912.1 1959179
#> M79T35_POS NA NA 2821550.2 1304875.3 2471336.1 3333582.7 2734244
#> M307T548_POS NA NA 410387.6 273687.8 288590.2 137297.5 NA
#> M183T224_POS NA NA NA NA NA 5059068.1 5147422
#> M349T47_POS NA NA 8730104.8 4105598.5 5141073.2 8424315.6 7896633
#> M182T828_POS 3761893 2572593 NA 3662819.1 5700534.8 4600172.4 5557015
#> PS4P4
#> M136T55_2_POS 1005418.8
#> M79T35_POS 3361452.3
#> M307T548_POS 271318.3
#> M183T224_POS NA
#> M349T47_POS 6441449.0
#> M182T828_POS 4433034.2
object@sample_info
#> sample_id injection.order class group
#> 1 Blank_3 1 Blank Blank
#> 2 Blank_4 2 Blank Blank
#> 3 QC_1 3 QC QC
#> 4 QC_2 4 QC QC
#> 5 PS4P1 5 Subject Subject
#> 6 PS4P2 6 Subject Subject
#> 7 PS4P3 7 Subject Subject
#> 8 PS4P4 8 Subject Subject
object@variable_info %>% head()
#> variable_id mz rt
#> 1 M136T55_2_POS 136.06140 54.97902
#> 2 M79T35_POS 79.05394 35.36550
#> 3 M307T548_POS 307.14035 547.56641
#> 4 M183T224_POS 183.06209 224.32777
#> 5 M349T47_POS 349.01584 47.00262
#> 6 M182T828_POS 181.99775 828.35712
object@sample_info_note
#> name meaning
#> 1 sample_id sample_id
#> 2 injection.order injection.order
#> 3 class class
#> 4 group group
object@variable_info_note
#> name meaning
#> 1 variable_id variable_id
#> 2 mz mz
#> 3 rt rt
object@process_info
#> $create_mass_dataset
#> --------------------
#> pacakge_name: massdataset
#> function_name: create_mass_dataset()
#> time: 2026-03-02 09:27:54.550095
#> parameters:
#> no : no
object@ms2_data
#> list()
object@annotation_table
#> data frame with 0 columns and 0 rowsSession 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