mass_dataset class object can also contain MS2 data.
Data preparation
mass_dataset class object
We need to create a mass_dataset class object first, see this document. And here we use the data from this step as an example.
load("feature_table/object_pos")
load("feature_table/object_neg")
MS2 data
The MS2 raw data should be converted to mgf format data. Please refer this document.
Here we use the demo data for tidymass, please download it and put it in the mgf_ms2_data folder.
Then uncompress it.
Add MS2 to mass_dataset class object
Positive mode.
library(massdataset)
object_pos2 =
mutate_ms2(
object = object_pos,
column = "rp",
polarity = "positive",
ms1.ms2.match.mz.tol = 10,
ms1.ms2.match.rt.tol = 15,
path = "mgf_ms2_data/POS/"
)
object_pos2
#> --------------------
#> massdataset version: 1.0.25
#> --------------------
#> 1.expression_data:[ 1612 x 36 data.frame]
#> 2.sample_info:[ 36 x 4 data.frame]
#> 36 samples:bl20210902_3 bl20210902_4 bl20210902_5 ... bl20210902_37 bl20210902_38
#> 3.variable_info:[ 1612 x 3 data.frame]
#> 1612 variables:M86T44_POS M90T638_POS M91T631_POS ... M1197T265_POS M1198T265_POS
#> 4.sample_info_note:[ 4 x 2 data.frame]
#> 5.variable_info_note:[ 3 x 2 data.frame]
#> 6.ms2_data:[ 9 variables x 9 MS2 spectra]
#> --------------------
#> Processing information
#> 2 processings in total
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2023-09-03 09:57:14
#> mutate_ms2 ----------
#> Package Function.used Time
#> 1 massdataset mutate_ms2() 2023-09-03 10:27:03
object_pos2@ms2_data
#> $`QEP_SGA_QC_posi_ms2_ce25_01.mgf;QEP_SGA_QC_posi_ms2_ce25_02.mgf;QEP_SGA_QC_posi_ms2_ce50_01.mgf;QEP_SGA_QC_posi_ms2_ce50_02.mgf`
#> --------------------
#> column: rp
#> polarity: positive
#> mz_tol: 10
#> rt_tol (second): 15
#> --------------------
#> 9 variables:
#> M103T92_POS M120T92_1_POS M133T255_POS M149T93_POS M166T94_POS...
#> 9 MS2 spectra.
#> mz103.054814801682rt96.92601 mz120.081003145403rt103.263636 mz133.101364135742rt269.674188 mz149.059844970703rt99.091818 mz166.086254683842rt103.128918...
Negative mode.
object_neg2 =
mutate_ms2(
object = object_neg,
column = "rp",
polarity = "negative",
ms1.ms2.match.mz.tol = 10,
ms1.ms2.match.rt.tol = 15,
path = "mgf_ms2_data/NEG/"
)
object_neg2@ms2_data
#> $`QEP_SGA_QC_neg_ms2_ce25_01.mgf;QEP_SGA_QC_neg_ms2_ce25_02.mgf;QEP_SGA_QC_neg_ms2_ce50_01.mgf;QEP_SGA_QC_neg_ms2_ce50_02.mgf`
#> --------------------
#> column: rp
#> polarity: negative
#> mz_tol: 10
#> rt_tol (second): 15
#> --------------------
#> 44 variables:
#> M101T106_NEG M116T626_NEG M116T505_NEG M116T483_NEG M116T586_NEG...
#> 43 MS2 spectra.
#> mz101.023086547852rt97.383582 mz115.919448852539rt634.03908 mz115.919456481934rt515.338818 mz115.919372558594rt471.106446 mz115.919403076172rt591.442212...
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] ggplot2_3.4.2 dplyr_1.1.2 magrittr_2.0.3 masstools_1.0.10
#> [5] 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] stringr_1.5.0 ProtGenerics_1.32.0
#> [13] pkgconfig_2.0.3 shape_1.4.6
#> [15] crayon_1.5.2 fastmap_1.1.1
#> [17] XVector_0.40.0 utf8_1.2.3
#> [19] rmarkdown_2.22 tzdb_0.4.0
#> [21] preprocessCore_1.62.1 purrr_1.0.1
#> [23] xfun_0.39 zlibbioc_1.46.0
#> [25] cachem_1.0.8 GenomeInfoDb_1.36.0
#> [27] jsonlite_1.8.5 DelayedArray_0.26.3
#> [29] BiocParallel_1.34.2 parallel_4.3.0
#> [31] cluster_2.1.4 R6_2.5.1
#> [33] stringi_1.7.12 bslib_0.5.0
#> [35] RColorBrewer_1.1-3 limma_3.56.2
#> [37] GenomicRanges_1.52.0 jquerylib_0.1.4
#> [39] Rcpp_1.0.10 bookdown_0.34
#> [41] SummarizedExperiment_1.30.2 iterators_1.0.14
#> [43] knitr_1.43 readr_2.1.4
#> [45] IRanges_2.34.0 Matrix_1.5-4
#> [47] tidyselect_1.2.0 rstudioapi_0.14
#> [49] yaml_2.3.7 doParallel_1.0.17
#> [51] codetools_0.2-19 affy_1.78.0
#> [53] blogdown_1.18.1 lattice_0.21-8
#> [55] tibble_3.2.1 plyr_1.8.8
#> [57] withr_2.5.0 Biobase_2.60.0
#> [59] evaluate_0.21 zip_2.3.0
#> [61] circlize_0.4.15 pillar_1.9.0
#> [63] affyio_1.70.0 BiocManager_1.30.21
#> [65] MatrixGenerics_1.12.2 foreach_1.5.2
#> [67] stats4_4.3.0 MSnbase_2.26.0
#> [69] MALDIquant_1.22.1 ncdf4_1.21
#> [71] generics_0.1.3 rprojroot_2.0.3
#> [73] RCurl_1.98-1.12 hms_1.1.3
#> [75] S4Vectors_0.38.1 munsell_0.5.0
#> [77] scales_1.2.1 glue_1.6.2
#> [79] tools_4.3.0 mzID_1.38.0
#> [81] vsn_3.68.0 mzR_2.34.0
#> [83] openxlsx_4.2.5.2 XML_3.99-0.14
#> [85] grid_4.3.0 impute_1.74.1
#> [87] tidyr_1.3.0 MsCoreUtils_1.12.0
#> [89] colorspace_2.1-0 GenomeInfoDbData_1.2.10
#> [91] cli_3.6.1 fansi_1.0.4
#> [93] S4Arrays_1.0.4 ComplexHeatmap_2.16.0
#> [95] pcaMethods_1.92.0 gtable_0.3.3
#> [97] sass_0.4.6 digest_0.6.31
#> [99] BiocGenerics_0.46.0 rjson_0.2.21
#> [101] htmltools_0.5.5 lifecycle_1.0.3
#> [103] here_1.0.1 GlobalOptions_0.1.2
#> [105] Rdisop_1.60.0 MASS_7.3-58.4