vignettes/mutate_ms2.Rmd
mutate_ms2.Rmdmass_data class object can also contain MS2 data.
We need to create a mass_data class object first, see
this document. And here we use the data from this step as
examples.
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 feature_table folder.
Then uncompress it.
mass_dataset class
object
Positive mode.
object_pos2 =
mutate_ms2(
object = object_pos,
column = "rp",
polarity = "positive",
ms1.ms2.match.mz.tol = 10,
ms1.ms2.match.rt.tol = 15,
path = "feature_table/MS2_data/POS/"
)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 = "feature_table/MS2_data/NEG/"
)
sessionInfo()
#> R version 4.2.1 (2022-06-23)
#> Platform: x86_64-apple-darwin17.0 (64-bit)
#> Running under: macOS Big Sur ... 10.16
#>
#> Matrix products: default
#> BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
#>
#> locale:
#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] masstools_1.0.2 forcats_0.5.1.9000 stringr_1.4.0 dplyr_1.0.9
#> [5] purrr_0.3.4 readr_2.1.2 tidyr_1.2.0 tibble_3.1.7
#> [9] ggplot2_3.3.6 tidyverse_1.3.1 magrittr_2.0.3 tinytools_0.9.1
#> [13] massdataset_1.0.12
#>
#> loaded via a namespace (and not attached):
#> [1] readxl_1.4.0 backports_1.4.1
#> [3] circlize_0.4.15 systemfonts_1.0.4
#> [5] plyr_1.8.7 lazyeval_0.2.2
#> [7] BiocParallel_1.30.3 GenomeInfoDb_1.32.2
#> [9] Rdisop_1.56.0 digest_0.6.29
#> [11] foreach_1.5.2 yulab.utils_0.0.5
#> [13] htmltools_0.5.2 fansi_1.0.3
#> [15] memoise_2.0.1 cluster_2.1.3
#> [17] doParallel_1.0.17 tzdb_0.3.0
#> [19] openxlsx_4.2.5 limma_3.52.2
#> [21] ComplexHeatmap_2.12.0 modelr_0.1.8
#> [23] matrixStats_0.62.0 pkgdown_2.0.5
#> [25] colorspace_2.0-3 rvest_1.0.2
#> [27] textshaping_0.3.6 haven_2.5.0
#> [29] xfun_0.31 crayon_1.5.1
#> [31] RCurl_1.98-1.7 jsonlite_1.8.0
#> [33] impute_1.70.0 iterators_1.0.14
#> [35] glue_1.6.2 gtable_0.3.0
#> [37] zlibbioc_1.42.0 XVector_0.36.0
#> [39] GetoptLong_1.0.5 DelayedArray_0.22.0
#> [41] shape_1.4.6 BiocGenerics_0.42.0
#> [43] scales_1.2.0 vsn_3.64.0
#> [45] DBI_1.1.3 Rcpp_1.0.8.3
#> [47] mzR_2.30.0 viridisLite_0.4.0
#> [49] clue_0.3-61 gridGraphics_0.5-1
#> [51] preprocessCore_1.58.0 stats4_4.2.1
#> [53] MsCoreUtils_1.8.0 htmlwidgets_1.5.4
#> [55] httr_1.4.3 RColorBrewer_1.1-3
#> [57] ellipsis_0.3.2 pkgconfig_2.0.3
#> [59] XML_3.99-0.10 sass_0.4.1
#> [61] dbplyr_2.2.1 utf8_1.2.2
#> [63] ggplotify_0.1.0 tidyselect_1.1.2
#> [65] rlang_1.0.3 munsell_0.5.0
#> [67] cellranger_1.1.0 tools_4.2.1
#> [69] cachem_1.0.6 cli_3.3.0
#> [71] generics_0.1.3 broom_1.0.0
#> [73] evaluate_0.15 fastmap_1.1.0
#> [75] mzID_1.34.0 yaml_2.3.5
#> [77] ragg_1.2.2 knitr_1.39
#> [79] fs_1.5.2 zip_2.2.0
#> [81] ncdf4_1.19 pbapply_1.5-0
#> [83] xml2_1.3.3 compiler_4.2.1
#> [85] rstudioapi_0.13 plotly_4.10.0
#> [87] png_0.1-7 affyio_1.66.0
#> [89] reprex_2.0.1 bslib_0.3.1
#> [91] stringi_1.7.6 desc_1.4.1
#> [93] MSnbase_2.22.0 lattice_0.20-45
#> [95] ProtGenerics_1.28.0 Matrix_1.4-1
#> [97] ggsci_2.9 vctrs_0.4.1
#> [99] pillar_1.7.0 lifecycle_1.0.1
#> [101] BiocManager_1.30.18 jquerylib_0.1.4
#> [103] MALDIquant_1.21 GlobalOptions_0.1.2
#> [105] data.table_1.14.2 bitops_1.0-7
#> [107] GenomicRanges_1.48.0 R6_2.5.1
#> [109] pcaMethods_1.88.0 affy_1.74.0
#> [111] IRanges_2.30.0 codetools_0.2-18
#> [113] MASS_7.3-57 assertthat_0.2.1
#> [115] SummarizedExperiment_1.26.1 rprojroot_2.0.3
#> [117] rjson_0.2.21 withr_2.5.0
#> [119] S4Vectors_0.34.0 GenomeInfoDbData_1.2.8
#> [121] parallel_4.2.1 hms_1.1.1
#> [123] grid_4.2.1 rmarkdown_2.14
#> [125] MatrixGenerics_1.8.1 Biobase_2.56.0
#> [127] lubridate_1.8.0