Identify metabolites based on MS1 or MS/MS database
Source:R/identify_metabolites_mass_dataset.R
identify_metabolites_mass_dataset.RdIdentify metabolites based on MS1 or MS/MS database.
![[Maturing]](figures/lifecycle-maturing.svg)
identify_metabolites_mass_dataset( object, ms1.match.ppm = 25, ms2.match.ppm = 30, mz.ppm.thr = 400, ms2.match.tol = 0.5, fraction.weight = 0.3, dp.forward.weight = 0.6, dp.reverse.weight = 0.1, rt.match.tol = 30, polarity = c("positive", "negative"), ce = "all", column = c("rp", "hilic"), ms1.match.weight = 0.25, rt.match.weight = 0.25, ms2.match.weight = 0.5, total.score.tol = 0.5, candidate.num = 3, database, threads = 3 )
Arguments
| object | A mass_dataset class obejct. |
|---|---|
| ms1.match.ppm | Precursor match ppm tolerance. |
| ms2.match.ppm | Fragment ion match ppm tolerance. |
| mz.ppm.thr | Accurate mass tolerance for m/z error calculation. |
| ms2.match.tol | MS2 match (MS2 similarity) tolerance. |
| fraction.weight | The weight for matched fragments. |
| dp.forward.weight | Forward dot product weight. |
| dp.reverse.weight | Reverse dot product weight. |
| rt.match.tol | RT match tolerance. |
| polarity | The polarity of data, "positive"or "negative". |
| ce | Collision energy. Please confirm the CE values in your database. Default is "all". |
| column | "hilic" (HILIC column) or "rp" (reverse phase). |
| ms1.match.weight | The weight of MS1 match for total score calculation. |
| rt.match.weight | The weight of RT match for total score calculation. |
| ms2.match.weight | The weight of MS2 match for total score calculation. |
| total.score.tol | Total score tolerance. The total score are referring to MS-DIAL. |
| candidate.num | The number of candidate. |
| database | MS2 database name or MS database. |
| threads | Number of threads |
Value
A metIdentifyClass object.
See also
The example and demo data of this function can be found https://tidymass.github.io/metid/articles/metid.html
Author
Xiaotao Shen shenxt1990@163.com
Examples
library(massdataset) #> ✓ magrittr 2.0.1 ✓ tinytools 0.9.1 #> massdataset, #> More information can be found at https://tidymass.github.io/massdataset/ #> Authors: Xiaotao Shen (shenxt@stanford.edu) #> Maintainer: Xiaotao Shen #> #> Attaching package: ‘massdataset’ #> The following object is masked from ‘package:stats’: #> #> filter library(tidyverse) #> ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ── #> ✓ ggplot2 3.3.5 ✓ purrr 0.3.4 #> ✓ tibble 3.1.3 ✓ dplyr 1.0.7 #> ✓ tidyr 1.1.3 ✓ stringr 1.4.0 #> ✓ readr 2.0.0 ✓ forcats 0.5.1 #> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ── #> x tidyr::extract() masks magrittr::extract() #> x dplyr::filter() masks massdataset::filter(), stats::filter() #> x dplyr::lag() masks stats::lag() #> x purrr::set_names() masks magrittr::set_names() ms1_data = readr::read_csv(file.path( system.file("ms1_peak", package = "metid"), "ms1.peak.table.csv" )) #> Rows: 100 Columns: 3 #> ── Column specification ──────────────────────────────────────────────────────── #> Delimiter: "," #> chr (1): name #> dbl (2): mz, rt #> #> ℹ Use `spec()` to retrieve the full column specification for this data. #> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message. 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: 0.01 #> -------------------- #> 1.expression_data:[ 100 x 2 data.frame] #> 2.sample_info:[ 2 x 4 data.frame] #> 3.variable_info:[ 100 x 3 data.frame] #> 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 (extract_process_info()) #> Creation ---------- #> Package Function.used Time #> 1 massdataset create_mass_dataset() 2021-12-22 00:24:53 data("hmdb_ms1_database0.0.3", package = "metid") object1 = identify_metabolites_mass_dataset(object = object, database = hmdb_ms1_database0.0.3) #> No MS2 data, so only use mz and/or RT for matching. #> You set rt.match.tol < 10,000, so if the compounds have RT, RTs will be used for matching #> | | | 0% | |======================= | 33% | |=============================================== | 67% | |======================================================================| 100% #> #> All done. #> data("snyder_database_rplc0.0.3", package = "metid") database = snyder_database_rplc0.0.3 object2 = identify_metabolites_mass_dataset(object = object1, database = snyder_database_rplc0.0.3) #> No MS2 data, so only use mz and/or RT for matching. #> You set rt.match.tol < 10,000, so if the compounds have RT, RTs will be used for matching #> | | | 0% | |======================= | 33% | |=============================================== | 67% | |======================================================================| 100% #> #> All done. #> head(object2@annotation_table) #> # A tibble: 6 × 18 #> variable_id ms2_files_id ms2_spectrum_id Compound.name CAS.ID HMDB.ID KEGG.ID #> <chr> <lgl> <lgl> <chr> <chr> <chr> <chr> #> 1 pRPLC_10319 NA NA Tyrosyl-Serine NA HMDB00… NA #> 2 pRPLC_10319 NA NA Seryltyrosine 21435… HMDB00… NA #> 3 pRPLC_10319 NA NA Monoglyceride… 36291… HMDB00… NA #> 4 pRPLC_1046 NA NA Theophylline NA HMDB00… NA #> 5 pRPLC_1046 NA NA Paraxanthine 611-5… HMDB01… C13747 #> 6 pRPLC_1046 NA NA Theobromine 83-67… HMDB02… C07480 #> # … with 11 more variables: Lab.ID <chr>, Adduct <chr>, mz.error <dbl>, #> # RT.error <dbl>, mz.match.score <dbl>, RT.match.score <dbl>, #> # Total.score <dbl>, CE <lgl>, SS <lgl>, Database <chr>, Level <dbl> extract_variable_info(object = object) #> variable_id mz rt #> 1 pRPLC_376 472.3032 772.906 #> 2 pRPLC_391 466.3292 746.577 #> 3 pRPLC_603 162.1125 33.746 #> 4 pRPLC_629 181.0720 36.360 #> 5 pRPLC_685 230.0701 158.205 #> 6 pRPLC_722 181.0721 228.305 #> 7 pRPLC_778 289.2275 286.185 #> 8 pRPLC_1046 181.0720 201.182 #> 9 pRPLC_1112 209.0922 57.406 #> 10 pRPLC_1148 282.8750 40.947 #> 11 pRPLC_1307 314.2326 401.848 #> 12 pRPLC_1326 207.1292 406.754 #> 13 pRPLC_1560 264.0558 495.824 #> 14 pRPLC_1799 699.3153 564.204 #> 15 pRPLC_1835 351.2139 572.258 #> 16 pRPLC_1860 249.1849 579.437 #> 17 pRPLC_2064 419.2343 621.835 #> 18 pRPLC_2065 660.4051 622.159 #> 19 pRPLC_2151 363.2198 638.691 #> 20 pRPLC_2171 568.3405 641.920 #> 21 pRPLC_2295 280.2637 669.073 #> 22 pRPLC_2386 659.3539 699.576 #> 23 pRPLC_2600 267.6469 820.049 #> 24 pRPLC_2615 508.3395 823.869 #> 25 pRPLC_3110 414.3002 545.186 #> 26 pRPLC_3138 286.1439 537.015 #> 27 pRPLC_3633 180.9620 40.947 #> 28 pRPLC_3839 141.0297 63.436 #> 29 pRPLC_3927 232.1545 77.507 #> 30 pRPLC_3959 153.0660 84.240 #> 31 pRPLC_3968 244.1545 87.115 #> 32 pRPLC_4027 797.7993 99.964 #> 33 pRPLC_4250 473.1718 171.891 #> 34 pRPLC_4508 260.1857 222.592 #> 35 pRPLC_4633 810.8984 240.055 #> 36 pRPLC_4673 379.2229 243.748 #> 37 pRPLC_5025 195.0878 284.564 #> 38 pRPLC_5044 286.2014 286.511 #> 39 pRPLC_5461 310.2011 337.952 #> 40 pRPLC_5721 517.1417 375.094 #> 41 pRPLC_5726 125.0600 376.112 #> 42 pRPLC_7083 229.0973 516.577 #> 43 pRPLC_7291 175.0968 536.688 #> 44 pRPLC_7637 563.1516 563.240 #> 45 pRPLC_7781 343.2266 572.579 #> 46 pRPLC_7987 105.0336 586.229 #> 47 pRPLC_8054 590.3301 590.498 #> 48 pRPLC_9043 651.4245 646.108 #> 49 pRPLC_9544 247.1694 682.229 #> 50 pRPLC_9960 232.1121 737.340 #> 51 pRPLC_10319 313.0773 790.835 #> 52 pRPLC_10514 343.1944 823.233 #> 53 pRPLC_10535 322.2266 823.869 #> 54 pRPLC_10563 313.0773 825.778 #> 55 pRPLC_10906 285.0097 901.116 #> 56 pRPLC_10992 566.4290 950.459 #> 57 pRPLC_11024 128.5944 963.771 #> 58 pRPLC_11039 392.2556 979.156 #> 59 pRPLC_11516 446.2952 860.152 #> 60 pRPLC_12426 599.3413 765.518 #> 61 pRPLC_12599 365.1958 750.413 #> 62 pRPLC_12912 539.2851 718.742 #> 63 pRPLC_13460 503.1783 680.306 #> 64 pRPLC_13514 858.5523 676.774 #> 65 pRPLC_13668 639.4079 667.146 #> 66 pRPLC_14733 533.9914 622.159 #> 67 pRPLC_15362 323.1850 596.649 #> 68 pRPLC_16044 302.2688 565.806 #> 69 pRPLC_16149 189.1005 561.272 #> 70 pRPLC_16169 395.2062 560.284 #> 71 pRPLC_16854 200.1728 524.606 #> 72 pRPLC_17684 625.2754 470.139 #> 73 pRPLC_17705 381.1375 468.480 #> 74 pRPLC_17826 499.0576 460.728 #> 75 pRPLC_17934 249.1099 452.073 #> 76 pRPLC_18346 316.1176 421.377 #> 77 pRPLC_18562 135.1170 407.078 #> 78 pRPLC_18580 196.1334 406.429 #> 79 pRPLC_18586 241.1410 406.429 #> 80 pRPLC_18600 493.4408 405.782 #> 81 pRPLC_19147 347.2037 363.045 #> 82 pRPLC_19371 164.1072 341.233 #> 83 pRPLC_19437 407.2250 336.002 #> 84 pRPLC_19565 313.0876 325.577 #> 85 pRPLC_19596 310.2011 322.275 #> 86 pRPLC_19688 125.0599 314.347 #> 87 pRPLC_19794 399.1861 304.420 #> 88 pRPLC_19991 419.0097 287.167 #> 89 pRPLC_20220 768.3619 268.005 #> 90 pRPLC_20346 209.0786 256.795 #> 91 pRPLC_20350 352.1653 256.480 #> 92 pRPLC_20625 301.0005 233.347 #> 93 pRPLC_21179 290.9476 152.554 #> 94 pRPLC_21343 426.0706 96.728 #> 95 pRPLC_21471 166.0726 77.507 #> 96 pRPLC_21522 123.0554 71.419 #> 97 pRPLC_21528 237.2214 70.460 #> 98 pRPLC_21720 170.0330 45.387 #> 99 pRPLC_21734 183.0628 44.440 #> 100 pRPLC_22098 182.0813 33.420