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: 1.0.34
#> --------------------
#> 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() 2024-09-25 21:02:23
In 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: 2024-09-25 21:02:23.082938
#> parameters:
#> no : no
Use slot() function
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: 2024-09-25 21:02:23.082938
#> 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: 2024-09-25 21:02:23.082938
#> parameters:
#> no : no
object@ms2_data
#> list()
object@annotation_table
#> data frame with 0 columns and 0 rows
Session information
sessionInfo()
#> R version 4.4.1 (2024-06-14)
#> Platform: aarch64-apple-darwin20
#> Running under: macOS 15.0
#>
#> Matrix products: default
#> BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
#>
#> locale:
#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.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_1.9.3 forcats_1.0.0 stringr_1.5.1 purrr_1.0.2
#> [5] readr_2.1.5 tidyr_1.3.1 tibble_3.2.1 tidyverse_2.0.0
#> [9] ggplot2_3.5.1 dplyr_1.1.4 magrittr_2.0.3 masstools_1.0.13
#> [13] massdataset_1.0.34
#>
#> loaded via a namespace (and not attached):
#> [1] bitops_1.0-8 pbapply_1.7-2
#> [3] remotes_2.5.0 rlang_1.1.4
#> [5] clue_0.3-65 GetoptLong_1.0.5
#> [7] matrixStats_1.3.0 compiler_4.4.1
#> [9] png_0.1-8 vctrs_0.6.5
#> [11] reshape2_1.4.4 ProtGenerics_1.36.0
#> [13] pkgconfig_2.0.3 shape_1.4.6.1
#> [15] crayon_1.5.3 fastmap_1.2.0
#> [17] XVector_0.44.0 utf8_1.2.4
#> [19] rmarkdown_2.28 tzdb_0.4.0
#> [21] preprocessCore_1.66.0 UCSC.utils_1.0.0
#> [23] xfun_0.47 MultiAssayExperiment_1.30.3
#> [25] zlibbioc_1.50.0 cachem_1.1.0
#> [27] GenomeInfoDb_1.40.1 jsonlite_1.8.8
#> [29] DelayedArray_0.30.1 BiocParallel_1.38.0
#> [31] parallel_4.4.1 cluster_2.1.6
#> [33] R6_2.5.1 bslib_0.8.0
#> [35] stringi_1.8.4 RColorBrewer_1.1-3
#> [37] limma_3.60.4 GenomicRanges_1.56.1
#> [39] jquerylib_0.1.4 Rcpp_1.0.13
#> [41] bookdown_0.40 SummarizedExperiment_1.34.0
#> [43] iterators_1.0.14 knitr_1.48
#> [45] IRanges_2.38.1 timechange_0.3.0
#> [47] Matrix_1.7-0 igraph_2.0.3
#> [49] tidyselect_1.2.1 rstudioapi_0.16.0
#> [51] abind_1.4-5 yaml_2.3.10
#> [53] affy_1.82.0 doParallel_1.0.17
#> [55] codetools_0.2-20 blogdown_1.19
#> [57] lattice_0.22-6 plyr_1.8.9
#> [59] withr_3.0.1 Biobase_2.64.0
#> [61] evaluate_0.24.0 zip_2.3.1
#> [63] circlize_0.4.16 BiocManager_1.30.25
#> [65] affyio_1.74.0 pillar_1.9.0
#> [67] MatrixGenerics_1.16.0 foreach_1.5.2
#> [69] stats4_4.4.1 MSnbase_2.30.1
#> [71] MALDIquant_1.22.3 ncdf4_1.23
#> [73] generics_0.1.3 RCurl_1.98-1.16
#> [75] rprojroot_2.0.4 hms_1.1.3
#> [77] S4Vectors_0.42.1 munsell_0.5.1
#> [79] scales_1.3.0 glue_1.7.0
#> [81] lazyeval_0.2.2 tools_4.4.1
#> [83] mzID_1.42.0 QFeatures_1.14.2
#> [85] vsn_3.72.0 mzR_2.38.0
#> [87] openxlsx_4.2.7 XML_3.99-0.17
#> [89] grid_4.4.1 impute_1.78.0
#> [91] MsCoreUtils_1.16.1 colorspace_2.1-1
#> [93] GenomeInfoDbData_1.2.12 PSMatch_1.8.0
#> [95] cli_3.6.3 fansi_1.0.6
#> [97] S4Arrays_1.4.1 ComplexHeatmap_2.20.0
#> [99] AnnotationFilter_1.28.0 pcaMethods_1.96.0
#> [101] gtable_0.3.5 sass_0.4.9
#> [103] digest_0.6.37 BiocGenerics_0.50.0
#> [105] SparseArray_1.4.8 rjson_0.2.22
#> [107] htmltools_0.5.8.1 lifecycle_1.0.4
#> [109] httr_1.4.7 here_1.0.1
#> [111] statmod_1.5.0 GlobalOptions_0.1.2
#> [113] MASS_7.3-61