有时我们只需要匹配一个峰值。可以使用 identify_single_peak() 函数来识别单个峰。
数据准备
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: 1.0.34
#> --------------------
#> 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() 2024-09-25 21:40:38
将 MS2 数据添加到 mass_dataset 对象
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] TRUE
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: 1.0.34
#> --------------------
#> 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() 2024-09-25 21:40:38
#> mutate_ms2 ----------
#> Package Function.used Time
#> 1 massdataset mutate_ms2() 2024-09-25 22:08:30
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...
注释单个峰值
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
将注释结果添加到对象
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
会话信息
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] stats4 stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] MSnbase_2.30.1 ProtGenerics_1.36.0 S4Vectors_0.42.1
#> [4] mzR_2.38.0 Rcpp_1.0.13 Biobase_2.64.0
#> [7] BiocGenerics_0.50.0 metid_1.2.34 lubridate_1.9.3
#> [10] forcats_1.0.0 stringr_1.5.1 purrr_1.0.2
#> [13] readr_2.1.5 tidyr_1.3.1 tibble_3.2.1
#> [16] tidyverse_2.0.0 ggplot2_3.5.1 dplyr_1.1.4
#> [19] magrittr_2.0.3 masstools_1.0.13 massdataset_1.0.34
#>
#> loaded via a namespace (and not attached):
#> [1] RColorBrewer_1.1-3 rstudioapi_0.16.0
#> [3] jsonlite_1.8.8 shape_1.4.6.1
#> [5] MultiAssayExperiment_1.30.3 MALDIquant_1.22.3
#> [7] rmarkdown_2.28 GlobalOptions_0.1.2
#> [9] zlibbioc_1.50.0 vctrs_0.6.5
#> [11] RCurl_1.98-1.16 blogdown_1.19
#> [13] progress_1.2.3 htmltools_0.5.8.1
#> [15] S4Arrays_1.4.1 cellranger_1.1.0
#> [17] SparseArray_1.4.8 mzID_1.42.0
#> [19] sass_0.4.9 parallelly_1.38.0
#> [21] bslib_0.8.0 htmlwidgets_1.6.4
#> [23] plyr_1.8.9 impute_1.78.0
#> [25] plotly_4.10.4 cachem_1.1.0
#> [27] igraph_2.0.3 lifecycle_1.0.4
#> [29] iterators_1.0.14 pkgconfig_2.0.3
#> [31] Matrix_1.7-0 R6_2.5.1
#> [33] fastmap_1.2.0 GenomeInfoDbData_1.2.12
#> [35] MatrixGenerics_1.16.0 future_1.34.0
#> [37] clue_0.3-65 digest_0.6.37
#> [39] pcaMethods_1.96.0 colorspace_2.1-1
#> [41] furrr_0.3.1 rprojroot_2.0.4
#> [43] GenomicRanges_1.56.1 fansi_1.0.6
#> [45] timechange_0.3.0 httr_1.4.7
#> [47] abind_1.4-5 compiler_4.4.1
#> [49] here_1.0.1 remotes_2.5.0
#> [51] bit64_4.0.5 withr_3.0.1
#> [53] doParallel_1.0.17 BiocParallel_1.38.0
#> [55] MASS_7.3-61 DelayedArray_0.30.1
#> [57] rjson_0.2.22 tools_4.4.1
#> [59] PSMatch_1.8.0 zip_2.3.1
#> [61] glue_1.7.0 QFeatures_1.14.2
#> [63] grid_4.4.1 cluster_2.1.6
#> [65] reshape2_1.4.4 generics_0.1.3
#> [67] gtable_0.3.5 tzdb_0.4.0
#> [69] preprocessCore_1.66.0 data.table_1.16.0
#> [71] hms_1.1.3 utf8_1.2.4
#> [73] XVector_0.44.0 foreach_1.5.2
#> [75] pillar_1.9.0 vroom_1.6.5
#> [77] limma_3.60.4 circlize_0.4.16
#> [79] lattice_0.22-6 bit_4.0.5
#> [81] tidyselect_1.2.1 ComplexHeatmap_2.20.0
#> [83] pbapply_1.7-2 knitr_1.48
#> [85] bookdown_0.40 IRanges_2.38.1
#> [87] SummarizedExperiment_1.34.0 xfun_0.47
#> [89] statmod_1.5.0 matrixStats_1.3.0
#> [91] stringi_1.8.4 UCSC.utils_1.0.0
#> [93] lazyeval_0.2.2 yaml_2.3.10
#> [95] evaluate_0.24.0 codetools_0.2-20
#> [97] MsCoreUtils_1.16.1 BiocManager_1.30.25
#> [99] cli_3.6.3 affyio_1.74.0
#> [101] munsell_0.5.1 jquerylib_0.1.4
#> [103] readxl_1.4.3 GenomeInfoDb_1.40.1
#> [105] globals_0.16.3 png_0.1-8
#> [107] XML_3.99-0.17 parallel_4.4.1
#> [109] prettyunits_1.2.0 AnnotationFilter_1.28.0
#> [111] bitops_1.0-8 listenv_0.9.1
#> [113] viridisLite_0.4.2 scales_1.3.0
#> [115] affy_1.82.0 openxlsx_4.2.7
#> [117] ncdf4_1.23 crayon_1.5.3
#> [119] GetoptLong_1.0.5 rlang_1.1.4
#> [121] vsn_3.72.0