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: 1.0.25
#> --------------------
#> 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() 2023-09-03 10:40:06
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: 2023-09-03 10:40:06.394313
#> 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: 2023-09-03 10:40:06.394313
#> 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: 2023-09-03 10:40:06.394313
#> parameters:
#> no : no
object@ms2_data
#> list()
object@annotation_table
#> data frame with 0 columns and 0 rows
Session information
sessionInfo()
#> R version 4.3.0 (2023-04-21)
#> Platform: x86_64-apple-darwin20 (64-bit)
#> Running under: macOS Ventura 13.5.1
#>
#> Matrix products: default
#> BLAS: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
#>
#> 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: America/Los_Angeles
#> tzcode source: internal
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] lubridate_1.9.2 forcats_1.0.0 stringr_1.5.0 purrr_1.0.1
#> [5] readr_2.1.4 tidyr_1.3.0 tibble_3.2.1 tidyverse_2.0.0
#> [9] ggplot2_3.4.2 dplyr_1.1.2 magrittr_2.0.3 masstools_1.0.10
#> [13] massdataset_1.0.25
#>
#> loaded via a namespace (and not attached):
#> [1] bitops_1.0-7 pbapply_1.7-0
#> [3] remotes_2.4.2 rlang_1.1.1
#> [5] clue_0.3-64 GetoptLong_1.0.5
#> [7] matrixStats_1.0.0 compiler_4.3.0
#> [9] png_0.1-8 vctrs_0.6.2
#> [11] ProtGenerics_1.32.0 pkgconfig_2.0.3
#> [13] shape_1.4.6 crayon_1.5.2
#> [15] fastmap_1.1.1 XVector_0.40.0
#> [17] utf8_1.2.3 rmarkdown_2.22
#> [19] tzdb_0.4.0 preprocessCore_1.62.1
#> [21] xfun_0.39 zlibbioc_1.46.0
#> [23] cachem_1.0.8 GenomeInfoDb_1.36.0
#> [25] jsonlite_1.8.5 DelayedArray_0.26.3
#> [27] BiocParallel_1.34.2 parallel_4.3.0
#> [29] cluster_2.1.4 R6_2.5.1
#> [31] stringi_1.7.12 bslib_0.5.0
#> [33] RColorBrewer_1.1-3 limma_3.56.2
#> [35] GenomicRanges_1.52.0 jquerylib_0.1.4
#> [37] Rcpp_1.0.10 bookdown_0.34
#> [39] SummarizedExperiment_1.30.2 iterators_1.0.14
#> [41] knitr_1.43 IRanges_2.34.0
#> [43] timechange_0.2.0 Matrix_1.5-4
#> [45] tidyselect_1.2.0 rstudioapi_0.14
#> [47] yaml_2.3.7 doParallel_1.0.17
#> [49] codetools_0.2-19 affy_1.78.0
#> [51] blogdown_1.18.1 lattice_0.21-8
#> [53] plyr_1.8.8 withr_2.5.0
#> [55] Biobase_2.60.0 evaluate_0.21
#> [57] zip_2.3.0 circlize_0.4.15
#> [59] pillar_1.9.0 affyio_1.70.0
#> [61] BiocManager_1.30.21 MatrixGenerics_1.12.2
#> [63] foreach_1.5.2 stats4_4.3.0
#> [65] MSnbase_2.26.0 MALDIquant_1.22.1
#> [67] ncdf4_1.21 generics_0.1.3
#> [69] rprojroot_2.0.3 RCurl_1.98-1.12
#> [71] hms_1.1.3 S4Vectors_0.38.1
#> [73] munsell_0.5.0 scales_1.2.1
#> [75] glue_1.6.2 tools_4.3.0
#> [77] mzID_1.38.0 vsn_3.68.0
#> [79] mzR_2.34.0 openxlsx_4.2.5.2
#> [81] XML_3.99-0.14 grid_4.3.0
#> [83] impute_1.74.1 MsCoreUtils_1.12.0
#> [85] colorspace_2.1-0 GenomeInfoDbData_1.2.10
#> [87] cli_3.6.1 fansi_1.0.4
#> [89] S4Arrays_1.0.4 ComplexHeatmap_2.16.0
#> [91] pcaMethods_1.92.0 gtable_0.3.3
#> [93] sass_0.4.6 digest_0.6.31
#> [95] BiocGenerics_0.46.0 rjson_0.2.21
#> [97] htmltools_0.5.5 lifecycle_1.0.3
#> [99] here_1.0.1 GlobalOptions_0.1.2
#> [101] Rdisop_1.60.0 MASS_7.3-58.4