Some times we only want to match one peak. We can use identify_single_peak() function to identify single peak.
Data preparation
library(massdataset)
library(tidyverse)
library(metid)
ms1_data =
readr::read_csv(file.path(
system.file("ms1_peak", package = "metid"),
"ms1.peak.table.csv"
))
ms1_data = data.frame(ms1_data, sample1 = 1, sample2 = 2)
expression_data = ms1_data %>%
dplyr::select(-c(name:rt))
variable_info =
ms1_data %>%
dplyr::select(name:rt) %>%
dplyr::rename(variable_id = name)
sample_info =
data.frame(
sample_id = colnames(expression_data),
injection.order = c(1, 2),
class = c("Subject", "Subject"),
group = c("Subject", "Subject")
)
rownames(expression_data) = variable_info$variable_id
object = create_mass_dataset(
expression_data = expression_data,
sample_info = sample_info,
variable_info = variable_info
)
object
#> --------------------
#> massdataset version: 1.0.25
#> --------------------
#> 1.expression_data:[ 100 x 2 data.frame]
#> 2.sample_info:[ 2 x 4 data.frame]
#> 2 samples:sample1 sample2
#> 3.variable_info:[ 100 x 3 data.frame]
#> 100 variables:pRPLC_376 pRPLC_391 pRPLC_603 ... pRPLC_21734 pRPLC_22098
#> 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() 2023-09-02 10:42:06
Add MS2 to mass_dataset object
path = "./metabolite_annotation"
dir.create(path)
ms2_data <- system.file("ms2_data", package = "metid")
file.copy(
from = file.path(ms2_data, "QC1_MSMS_NCE25.mgf"),
to = path,
overwrite = TRUE,
recursive = TRUE
)
#> [1] FALSE
object =
massdataset::mutate_ms2(
object = object,
column = "rp",
polarity = "positive",
ms1.ms2.match.mz.tol = 10,
ms1.ms2.match.rt.tol = 30,
path = path
)
object
#> --------------------
#> massdataset version: 1.0.25
#> --------------------
#> 1.expression_data:[ 100 x 2 data.frame]
#> 2.sample_info:[ 2 x 4 data.frame]
#> 2 samples:sample1 sample2
#> 3.variable_info:[ 100 x 3 data.frame]
#> 100 variables:pRPLC_376 pRPLC_391 pRPLC_603 ... pRPLC_21734 pRPLC_22098
#> 4.sample_info_note:[ 4 x 2 data.frame]
#> 5.variable_info_note:[ 3 x 2 data.frame]
#> 6.ms2_data:[ 16 variables x 16 MS2 spectra]
#> --------------------
#> Processing information
#> 2 processings in total
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2023-09-02 10:42:06
#> mutate_ms2 ----------
#> Package Function.used Time
#> 1 massdataset mutate_ms2() 2023-09-02 10:52:12
object@ms2_data
#> $QC1_MSMS_NCE25.mgf
#> --------------------
#> column: rp
#> polarity: positive
#> mz_tol: 10
#> rt_tol (second): 30
#> --------------------
#> 16 variables:
#> pRPLC_603 pRPLC_1112 pRPLC_1307 pRPLC_1860 pRPLC_3927...
#> 16 MS2 spectra.
#> mz162.112344859731rt35.281947 mz209.092013478675rt62.786886 mz314.232098215226rt415.887162 mz249.184679448673rt585.115404 mz232.154251098633rt84.328626...
Annotate single peaks
data("snyder_database_rplc0.0.3", package = "metid")
annotate_single_peak_mass_dataset(
object = object,
variable_index = 3,
based_on_rt = FALSE,
based_on_ms2 = FALSE,
database = snyder_database_rplc0.0.3,
add_to_annotation_table = FALSE
)
#> variable_id ms2_files_id ms2_spectrum_id Compound.name CAS.ID HMDB.ID
#> 1 pRPLC_603 NA NA L(-)-Carnitine <NA> <NA>
#> 2 pRPLC_603 NA NA L-Carnitine 541-15-1 HMDB00062
#> KEGG.ID Lab.ID Adduct mz.error mz.match.score RT.error RT.match.score CE SS
#> 1 <NA> RPLC_54 (M+H)+ 0.05375 0.9999977 NA NA NA NA
#> 2 C00318 RPLC_406 (M+H)+ 1.86625 0.9972176 NA NA NA NA
#> Total.score Database Level
#> 1 0.9999977 MS_0.0.2 3
#> 2 0.9972176 MS_0.0.2 3
annotate_single_peak_mass_dataset(
object = object,
variable_index = 3,
based_on_rt = TRUE,
based_on_ms2 = FALSE,
database = snyder_database_rplc0.0.3,
add_to_annotation_table = FALSE
)
#> variable_id ms2_files_id ms2_spectrum_id Compound.name CAS.ID HMDB.ID
#> 1 pRPLC_603 NA NA L(-)-Carnitine <NA> <NA>
#> 2 pRPLC_603 NA NA L-Carnitine 541-15-1 HMDB00062
#> KEGG.ID Lab.ID Adduct mz.error mz.match.score RT.error RT.match.score CE SS
#> 1 <NA> RPLC_54 (M+H)+ 0.05375 0.9999977 0.746 0.9996909 NA NA
#> 2 C00318 RPLC_406 (M+H)+ 1.86625 0.9972176 2.254 0.9971815 NA NA
#> Total.score Database Level
#> 1 0.9998443 MS_0.0.2 2
#> 2 0.9971995 MS_0.0.2 2
annotate_single_peak_mass_dataset(
object = object,
variable_index = 3,
based_on_rt = TRUE,
based_on_ms2 = TRUE,
database = snyder_database_rplc0.0.3,
add_to_annotation_table = FALSE
)
#>
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#> variable_id ms2_files_id ms2_spectrum_id Compound.name
#> 1 pRPLC_603 QC1_MSMS_NCE25.mgf mz162.112344859731rt35.281947 L-Carnitine
#> CAS.ID HMDB.ID KEGG.ID Lab.ID Adduct mz.error mz.match.score RT.error
#> 1 541-15-1 HMDB00062 C00318 RPLC_406 (M+H)+ 1.424649 0.9983776 0.718053
#> RT.match.score CE SS Total.score Database Level
#> 1 0.9997136 NCE25 0.5995589 0.7993023 MS_0.0.2 1
Add annotation result to object
object@annotation_table
#> data frame with 0 columns and 0 rows
object1 =
annotate_single_peak_mass_dataset(
object = object,
variable_index = 3,
based_on_rt = FALSE,
based_on_ms2 = FALSE,
database = snyder_database_rplc0.0.3,
add_to_annotation_table = TRUE
)
object1@annotation_table
#> variable_id ms2_files_id ms2_spectrum_id Compound.name CAS.ID HMDB.ID
#> 1 pRPLC_603 NA NA L(-)-Carnitine <NA> <NA>
#> 2 pRPLC_603 NA NA L-Carnitine 541-15-1 HMDB00062
#> KEGG.ID Lab.ID Adduct mz.error mz.match.score RT.error RT.match.score CE SS
#> 1 <NA> RPLC_54 (M+H)+ 0.05375 0.9999977 NA NA NA NA
#> 2 C00318 RPLC_406 (M+H)+ 1.86625 0.9972176 NA NA NA NA
#> Total.score Database Level
#> 1 0.9999977 MS_0.0.2 3
#> 2 0.9972176 MS_0.0.2 3
object2 =
annotate_single_peak_mass_dataset(
object = object1,
variable_index = 3,
based_on_rt = TRUE,
based_on_ms2 = FALSE,
database = snyder_database_rplc0.0.3,
add_to_annotation_table = TRUE
)
object2@annotation_table
#> variable_id ms2_files_id ms2_spectrum_id Compound.name CAS.ID HMDB.ID
#> 1 pRPLC_603 NA NA L(-)-Carnitine <NA> <NA>
#> 2 pRPLC_603 NA NA L-Carnitine 541-15-1 HMDB00062
#> 3 pRPLC_603 NA NA L(-)-Carnitine <NA> <NA>
#> KEGG.ID Lab.ID Adduct mz.error mz.match.score RT.error RT.match.score CE SS
#> 1 <NA> RPLC_54 (M+H)+ 0.05375 0.9999977 0.746 0.9996909 NA NA
#> 2 C00318 RPLC_406 (M+H)+ 1.86625 0.9972176 2.254 0.9971815 NA NA
#> 3 <NA> RPLC_54 (M+H)+ 0.05375 0.9999977 NA NA NA NA
#> Total.score Database Level
#> 1 0.9998443 MS_0.0.2 2
#> 2 0.9971995 MS_0.0.2 2
#> 3 0.9999977 MS_0.0.2 3
object3 =
annotate_single_peak_mass_dataset(
object = object2,
variable_index = 3,
based_on_rt = TRUE,
based_on_ms2 = FALSE,
database = snyder_database_rplc0.0.3,
add_to_annotation_table = TRUE
)
object3@annotation_table
#> variable_id ms2_files_id ms2_spectrum_id Compound.name CAS.ID HMDB.ID
#> 1 pRPLC_603 NA NA L(-)-Carnitine <NA> <NA>
#> 2 pRPLC_603 NA NA L-Carnitine 541-15-1 HMDB00062
#> KEGG.ID Lab.ID Adduct mz.error mz.match.score RT.error RT.match.score CE SS
#> 1 <NA> RPLC_54 (M+H)+ 0.05375 0.9999977 0.746 0.9996909 NA NA
#> 2 C00318 RPLC_406 (M+H)+ 1.86625 0.9972176 2.254 0.9971815 NA NA
#> Total.score Database Level
#> 1 0.9998443 MS_0.0.2 2
#> 2 0.9971995 MS_0.0.2 2
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] grid stats4 stats graphics grDevices utils datasets
#> [8] methods base
#>
#> other attached packages:
#> [1] lubridate_1.9.2 forcats_1.0.0 stringr_1.5.0
#> [4] purrr_1.0.1 readr_2.1.4 tibble_3.2.1
#> [7] tidyverse_2.0.0 metid_1.2.29 metpath_1.0.8
#> [10] ComplexHeatmap_2.16.0 mixOmics_6.24.0 lattice_0.21-8
#> [13] MASS_7.3-58.4 massstat_1.0.5 tidyr_1.3.0
#> [16] ggfortify_0.4.16 massqc_1.0.6 masscleaner_1.0.11
#> [19] xcms_3.22.0 MSnbase_2.26.0 ProtGenerics_1.32.0
#> [22] S4Vectors_0.38.1 mzR_2.34.0 Rcpp_1.0.10
#> [25] Biobase_2.60.0 BiocGenerics_0.46.0 BiocParallel_1.34.2
#> [28] massprocesser_1.0.10 ggplot2_3.4.2 dplyr_1.1.2
#> [31] magrittr_2.0.3 masstools_1.0.10 massdataset_1.0.25
#> [34] tidymass_1.0.8
#>
#> loaded via a namespace (and not attached):
#> [1] splines_4.3.0 bitops_1.0-7
#> [3] cellranger_1.1.0 polyclip_1.10-4
#> [5] preprocessCore_1.62.1 XML_3.99-0.14
#> [7] rpart_4.1.19 fastDummies_1.6.3
#> [9] lifecycle_1.0.3 doParallel_1.0.17
#> [11] rprojroot_2.0.3 globals_0.16.2
#> [13] backports_1.4.1 plotly_4.10.2
#> [15] openxlsx_4.2.5.2 limma_3.56.2
#> [17] Hmisc_5.1-0 sass_0.4.6
#> [19] rmarkdown_2.22 jquerylib_0.1.4
#> [21] yaml_2.3.7 remotes_2.4.2
#> [23] doRNG_1.8.6 zip_2.3.0
#> [25] MsCoreUtils_1.12.0 pbapply_1.7-0
#> [27] RColorBrewer_1.1-3 zlibbioc_1.46.0
#> [29] GenomicRanges_1.52.0 ggraph_2.1.0
#> [31] itertools_0.1-3 RCurl_1.98-1.12
#> [33] nnet_7.3-18 tweenr_2.0.2
#> [35] circlize_0.4.15 GenomeInfoDbData_1.2.10
#> [37] IRanges_2.34.0 ggrepel_0.9.3
#> [39] listenv_0.9.0 ellipse_0.4.5
#> [41] RSpectra_0.16-1 missForest_1.5
#> [43] parallelly_1.36.0 ncdf4_1.21
#> [45] codetools_0.2-19 DelayedArray_0.26.3
#> [47] ggforce_0.4.1 tidyselect_1.2.0
#> [49] shape_1.4.6 farver_2.1.1
#> [51] viridis_0.6.3 matrixStats_1.0.0
#> [53] base64enc_0.1-3 jsonlite_1.8.5
#> [55] GetoptLong_1.0.5 multtest_2.56.0
#> [57] e1071_1.7-13 tidygraph_1.2.3
#> [59] Formula_1.2-5 survival_3.5-5
#> [61] iterators_1.0.14 foreach_1.5.2
#> [63] progress_1.2.2 tools_4.3.0
#> [65] glue_1.6.2 rARPACK_0.11-0
#> [67] gridExtra_2.3 xfun_0.39
#> [69] here_1.0.1 MatrixGenerics_1.12.2
#> [71] GenomeInfoDb_1.36.0 withr_2.5.0
#> [73] BiocManager_1.30.21 fastmap_1.1.1
#> [75] fansi_1.0.4 blogdown_1.18.1
#> [77] digest_0.6.31 timechange_0.2.0
#> [79] R6_2.5.1 colorspace_2.1-0
#> [81] utf8_1.2.3 generics_0.1.3
#> [83] data.table_1.14.8 corpcor_1.6.10
#> [85] robustbase_0.95-1 class_7.3-21
#> [87] graphlayouts_1.0.0 prettyunits_1.1.1
#> [89] httr_1.4.6 htmlwidgets_1.6.2
#> [91] S4Arrays_1.0.4 pkgconfig_2.0.3
#> [93] gtable_0.3.3 robust_0.7-1
#> [95] impute_1.74.1 MassSpecWavelet_1.66.0
#> [97] XVector_0.40.0 furrr_0.3.1
#> [99] pcaPP_2.0-3 htmltools_0.5.5
#> [101] bookdown_0.34 MALDIquant_1.22.1
#> [103] clue_0.3-64 scales_1.2.1
#> [105] png_0.1-8 knitr_1.43
#> [107] rstudioapi_0.14 reshape2_1.4.4
#> [109] tzdb_0.4.0 rjson_0.2.21
#> [111] checkmate_2.2.0 ggcorrplot_0.1.4
#> [113] proxy_0.4-27 cachem_1.0.8
#> [115] GlobalOptions_0.1.2 parallel_4.3.0
#> [117] foreign_0.8-84 mzID_1.38.0
#> [119] vsn_3.68.0 pillar_1.9.0
#> [121] vctrs_0.6.2 MsFeatures_1.8.0
#> [123] RANN_2.6.1 pcaMethods_1.92.0
#> [125] randomForest_4.7-1.1 cluster_2.1.4
#> [127] htmlTable_2.4.1 evaluate_0.21
#> [129] mvtnorm_1.2-2 cli_3.6.1
#> [131] compiler_4.3.0 rlang_1.1.1
#> [133] crayon_1.5.2 rngtools_1.5.2
#> [135] Rdisop_1.60.0 rrcov_1.7-3
#> [137] affy_1.78.0 plyr_1.8.8
#> [139] stringi_1.7.12 viridisLite_0.4.2
#> [141] Biostrings_2.68.1 munsell_0.5.0
#> [143] lazyeval_0.2.2 fit.models_0.64
#> [145] Matrix_1.5-4 hms_1.1.3
#> [147] patchwork_1.1.2 future_1.32.0
#> [149] KEGGREST_1.40.0 SummarizedExperiment_1.30.2
#> [151] igraph_1.4.3 affyio_1.70.0
#> [153] bslib_0.5.0 DEoptimR_1.0-14
#> [155] readxl_1.4.2