Data preparation
We just use the dataset which are from previous step.
library(tidymass)
#> Registered S3 method overwritten by 'Hmisc':
#> method from
#> vcov.default fit.models
#> ── Attaching packages ───────────────────────────── tidymass 0.99.6 ──
#> ✓ massdataset 0.99.20 ✓ metpath 0.99.4
#> ✓ massprocesser 0.99.3 ✓ metid 1.2.4
#> ✓ masscleaner 0.99.7 ✓ masstools 0.99.5
#> ✓ massqc 0.99.7 ✓ dplyr 1.0.8
#> ✓ massstat 0.99.13 ✓ ggplot2 3.3.5
#> ── Conflicts ───────────────────────────────── tidymass_conflicts() ──
#> x massdataset::apply() masks base::apply()
#> x dplyr::collect() masks xcms::collect()
#> x BiocGenerics::colMeans() masks massdataset::colMeans(), base::colMeans()
#> x BiocGenerics::colSums() masks massdataset::colSums(), base::colSums()
#> x dplyr::combine() masks MSnbase::combine(), Biobase::combine(), BiocGenerics::combine()
#> x dplyr::filter() masks metpath::filter(), massdataset::filter(), stats::filter()
#> x dplyr::first() masks S4Vectors::first()
#> x dplyr::groups() masks xcms::groups()
#> x S4Vectors::intersect() masks BiocGenerics::intersect(), massdataset::intersect(), base::intersect()
#> x dplyr::lag() masks stats::lag()
#> x masstools::mz_rt_match() masks massdataset::mz_rt_match()
#> x dplyr::rename() masks S4Vectors::rename(), massdataset::rename()
#> x BiocGenerics::rowMeans() masks massdataset::rowMeans(), base::rowMeans()
#> x BiocGenerics::rowSums() masks massdataset::rowSums(), base::rowSums()
load("data_cleaning/POS/object_pos2")
load("data_cleaning/NEG/object_neg2")
Add MS2 spectra data to mass_dataset class
Download the MS2 data here.
Uncompress it.
Positive mode 🔗︎
object_pos2 <-
mutate_ms2(
object = object_pos2,
column = "rp",
polarity = "positive",
ms1.ms2.match.mz.tol = 15,
ms1.ms2.match.rt.tol = 30,
path = "mgf_ms2_data/POS"
)
#> Reading mgf data...
#>
#> Reading mgf data...
#>
#> Reading mgf data...
#>
#> Reading mgf data...
#> 1042 out of 5101 variable have MS2 spectra.
#> Selecting the most intense MS2 spectrum for each peak...
object_pos2
#> --------------------
#> massdataset version: 0.99.8
#> --------------------
#> 1.expression_data:[ 5101 x 259 data.frame]
#> 2.sample_info:[ 259 x 6 data.frame]
#> 3.variable_info:[ 5101 x 6 data.frame]
#> 4.sample_info_note:[ 6 x 2 data.frame]
#> 5.variable_info_note:[ 6 x 2 data.frame]
#> 6.ms2_data:[ 1042 variables x 951 MS2 spectra]
#> --------------------
#> Processing information (extract_process_info())
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2022-02-22 16:37:06
#> process_data ----------
#> Package Function.used Time
#> 1 massprocesser process_data 2022-02-22 16:36:42
#> mutate ----------
#> Package Function.used Time
#> 1 massdataset mutate() 2022-03-10 22:47:29
#> mutate_variable_na_freq ----------
#> Package Function.used Time
#> 1 massdataset mutate_variable_na_freq() 2022-03-10 22:47:33
#> 2 massdataset mutate_variable_na_freq() 2022-03-10 22:47:33
#> 3 massdataset mutate_variable_na_freq() 2022-03-10 22:47:33
#> filter ----------
#> Package Function.used Time
#> 1 massdataset filter() 2022-03-10 22:47:34
#> impute_mv ----------
#> Package Function.used Time
#> 1 masscleaner impute_mv() 2022-03-10 22:47:48
#> normalize_data ----------
#> Package Function.used Time
#> 1 masscleaner normalize_data() 2022-03-10 22:47:53
#> integrate_data ----------
#> Package Function.used Time
#> 1 masscleaner integrate_data() 2022-03-10 22:47:53
#> mutate_ms2 ----------
#> Package Function.used Time
#> 1 massdataset mutate_ms2() 2022-03-10 23:48:26
extract_ms2_data(object_pos2)
#> $`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: 15
#> rt_tol (second): 30
#> --------------------
#> 1042 variables:
#> M71T775_POS M72T53_POS M83T50_POS M84T57_POS M85T54_POS...
#> 951 MS2 spectra.
#> mz70.981170654297rt775.4286 mz72.081642150879rt53.6528862 mz82.945625305176rt49.238013 mz84.045127868652rt59.6895132 mz85.029016959043rt53.0835648...
Negative mode 🔗︎
object_neg2 <-
mutate_ms2(
object = object_neg2,
column = "rp",
polarity = "negative",
ms1.ms2.match.mz.tol = 15,
ms1.ms2.match.rt.tol = 30,
path = "mgf_ms2_data/NEG"
)
#> Reading mgf data...
#>
#> Reading mgf data...
#>
#> Reading mgf data...
#>
#> Reading mgf data...
#> 1092 out of 4104 variable have MS2 spectra.
#> Selecting the most intense MS2 spectrum for each peak...
object_neg2
#> --------------------
#> massdataset version: 0.99.8
#> --------------------
#> 1.expression_data:[ 4104 x 259 data.frame]
#> 2.sample_info:[ 259 x 6 data.frame]
#> 3.variable_info:[ 4104 x 6 data.frame]
#> 4.sample_info_note:[ 6 x 2 data.frame]
#> 5.variable_info_note:[ 6 x 2 data.frame]
#> 6.ms2_data:[ 1092 variables x 988 MS2 spectra]
#> --------------------
#> Processing information (extract_process_info())
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2022-02-22 16:38:19
#> process_data ----------
#> Package Function.used Time
#> 1 massprocesser process_data 2022-02-22 16:38:02
#> mutate ----------
#> Package Function.used Time
#> 1 massdataset mutate() 2022-03-10 22:47:29
#> mutate_variable_na_freq ----------
#> Package Function.used Time
#> 1 massdataset mutate_variable_na_freq() 2022-03-10 22:47:35
#> 2 massdataset mutate_variable_na_freq() 2022-03-10 22:47:35
#> 3 massdataset mutate_variable_na_freq() 2022-03-10 22:47:35
#> filter ----------
#> Package Function.used Time
#> 1 massdataset filter() 2022-03-10 22:47:36
#> impute_mv ----------
#> Package Function.used Time
#> 1 masscleaner impute_mv() 2022-03-10 22:47:52
#> normalize_data ----------
#> Package Function.used Time
#> 1 masscleaner normalize_data() 2022-03-10 22:47:57
#> integrate_data ----------
#> Package Function.used Time
#> 1 masscleaner integrate_data() 2022-03-10 22:47:57
#> mutate_ms2 ----------
#> Package Function.used Time
#> 1 massdataset mutate_ms2() 2022-03-10 23:48:52
extract_ms2_data(object_neg2)
#> $`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: 15
#> rt_tol (second): 30
#> --------------------
#> 1092 variables:
#> M71T51_NEG M73T74_NEG M75T52_NEG M80T299_NEG M80T232_NEG...
#> 988 MS2 spectra.
#> mz71.012359619141rt52.3270968 mz73.02799987793rt74.779476 mz75.007308959961rt24.1557228 mz79.955728954783rt301.268466 mz79.955834350356rt235.127328...
Metabolite annotation
Metabolite annotation is based on the metid package.
Download database 🔗︎
We need to download MS2 database from metid website.
Here we download the Michael Snyder RPLC databases, Orbitrap database and MoNA database. And place them in a new folder named as metabolite_annotation.
Positive mode 🔗︎
Annotate features using snyder_database_rplc0.0.3. 🔗︎
load("metabolite_annotation/snyder_database_rplc0.0.3.rda")
snyder_database_rplc0.0.3
#> -----------Base information------------
#> Version: 0.0.2
#> Source: MS
#> Link: http://snyderlab.stanford.edu/
#> Creater: Xiaotao Shen ( shenxt1990@163.com )
#> With RT information
#> -----------Spectral information------------
#> There are 14 items of metabolites in database:
#> Lab.ID; Compound.name; mz; RT; CAS.ID; HMDB.ID; KEGG.ID; Formula; mz.pos; mz.neg; Submitter; Family; Sub.pathway; Note
#> There are 833 metabolites in total
#> There are 356 metabolites in positive mode with MS2 spectra.
#> There are 534 metabolites in negative mode with MS2 spectra.
#> Collision energy in positive mode (number:):
#> Total number: 2
#> NCE25; NCE50
#> Collision energy in negative mode:
#> Total number: 2
#> NCE25; NCE50
object_pos2 <-
annotate_metabolites_mass_dataset(object = object_pos2,
ms1.match.ppm = 15,
rt.match.tol = 30,
polarity = "positive",
database = snyder_database_rplc0.0.3)
Annotate features using orbitrap_database0.0.3. 🔗︎
load("metabolite_annotation/orbitrap_database0.0.3.rda")
orbitrap_database0.0.3
#> -----------Base information------------
#> Version: 0.0.1
#> Source: NIST
#> Link: https://www.nist.gov/
#> Creater: Xiaotao Shen ( shenxt1990@163.com )
#> Without RT informtaion
#> -----------Spectral information------------
#> There are 8 items of metabolites in database:
#> Lab.ID; Compound.name; mz; RT; CAS.ID; HMDB.ID; KEGG.ID; Formula
#> There are 8360 metabolites in total
#> There are 7103 metabolites in positive mode with MS2 spectra.
#> There are 3311 metabolites in negative mode with MS2 spectra.
#> Collision energy in positive mode (number:):
#> Total number: 12
#> 10; 15; 45; 55; 5; 20; 30; 35; 40; 25
#> Collision energy in negative mode:
#> Total number: 12
#> 10; 25; 5; 15; 20; 30; 50; 35; 40; 45
object_pos2 <-
annotate_metabolites_mass_dataset(object = object_pos2,
ms1.match.ppm = 15,
polarity = "positive",
database = orbitrap_database0.0.3)
Annotate features using mona_database0.0.3. 🔗︎
load("metabolite_annotation/mona_database0.0.3.rda")
orbitrap_database0.0.3
#> -----------Base information------------
#> Version: 0.0.1
#> Source: NIST
#> Link: https://www.nist.gov/
#> Creater: Xiaotao Shen ( shenxt1990@163.com )
#> Without RT informtaion
#> -----------Spectral information------------
#> There are 8 items of metabolites in database:
#> Lab.ID; Compound.name; mz; RT; CAS.ID; HMDB.ID; KEGG.ID; Formula
#> There are 8360 metabolites in total
#> There are 7103 metabolites in positive mode with MS2 spectra.
#> There are 3311 metabolites in negative mode with MS2 spectra.
#> Collision energy in positive mode (number:):
#> Total number: 12
#> 10; 15; 45; 55; 5; 20; 30; 35; 40; 25
#> Collision energy in negative mode:
#> Total number: 12
#> 10; 25; 5; 15; 20; 30; 50; 35; 40; 45
object_pos2 <-
annotate_metabolites_mass_dataset(object = object_pos2,
ms1.match.ppm = 15,
polarity = "positive",
database = mona_database0.0.3)
Negative mode 🔗︎
Annotate features using snyder_database_rplc0.0.3. 🔗︎
object_neg2 <-
annotate_metabolites_mass_dataset(object = object_neg2,
ms1.match.ppm = 15,
rt.match.tol = 30,
polarity = "negative",
database = snyder_database_rplc0.0.3)
Annotate features using orbitrap_database0.0.3. 🔗︎
object_neg2 <-
annotate_metabolites_mass_dataset(object = object_neg2,
ms1.match.ppm = 15,
polarity = "negative",
database = orbitrap_database0.0.3)
Annotate features using mona_database0.0.3. 🔗︎
object_neg2 <-
annotate_metabolites_mass_dataset(object = object_neg2,
ms1.match.ppm = 15,
polarity = "negative",
database = mona_database0.0.3)
Annotation result 🔗︎
The annotation result will assign into mass_dataset class as the annotation_table slot.
head(extract_annotation_table(object = object_pos2))
#> variable_id
#> 1 M100T160_POS
#> 2 M103T100_POS
#> 3 M103T100_POS
#> 4 M104T51_POS
#> 5 M113T187_POS
#> 6 M113T81_POS
#> ms2_files_id
#> 1 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
#> 2 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
#> 3 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
#> 4 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
#> 5 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
#> 6 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
#> ms2_spectrum_id Compound.name CAS.ID HMDB.ID
#> 1 mz100.076248168945rt158.377638 N-Methyl-2-pyrrolidone 872-50-4 <NA>
#> 2 mz103.054814801682rt96.92601 Phenylacetaldehyde 122-78-1 HMDB06236
#> 3 mz103.054814801682rt96.92601 3-Amino-2-oxazolidinone 80-65-9 <NA>
#> 4 mz104.107467651367rt49.510314 5-Amino-1-pentanol 2508-29-4 <NA>
#> 5 mz113.060150146484rt188.406384 1,4-Cyclohexanedione <NA> <NA>
#> 6 mz113.035087585449rt77.20827 URACIL <NA> <NA>
#> KEGG.ID Lab.ID Adduct mz.error mz.match.score RT.error
#> 1 C11118 MONA_11509 (M+H)+ 1.335652 0.9960435 NA
#> 2 C00601 NO07389 (M+H-H2O)+ 1.537004 0.9947640 NA
#> 3 <NA> NO07231 (M+H)+ 11.537004 0.7439487 NA
#> 4 <NA> NO07238 (M+H)+ 1.169128 0.9969671 NA
#> 5 <NA> MONA_14519 (M+H)+ 1.051626 0.9975454 NA
#> 6 <NA> MONA_18148 (M+H)+ 1.275544 0.9963909 NA
#> RT.match.score CE SS Total.score Database Level
#> 1 NA 35 (nominal) 0.6871252 0.8029696 MoNA_0.0.1 2
#> 2 NA 10 0.5748835 0.7323387 NIST_0.0.1 2
#> 3 NA 20 0.5020256 0.5927468 NIST_0.0.1 2
#> 4 NA 5 0.5971697 0.7470937 NIST_0.0.1 2
#> 5 NA HCD (NCE 20-30-40%) 0.5401414 0.7116679 MoNA_0.0.1 2
#> 6 NA 10 0.6889885 0.8042644 MoNA_0.0.1 2
variable_info_pos <-
extract_variable_info(object = object_pos2)
head(variable_info_pos)
#> variable_id mz rt na_freq na_freq.1 na_freq.2 Compound.name
#> 1 M70T53_POS 70.06596 52.78542 0.00000000 0.14545455 0.00000000 <NA>
#> 2 M70T527_POS 70.36113 526.76657 0.02564103 0.18181818 0.30000000 <NA>
#> 3 M71T775_POS 70.98125 775.44867 0.00000000 0.00000000 0.00000000 <NA>
#> 4 M71T669_POS 70.98125 668.52844 0.00000000 0.02727273 0.01818182 <NA>
#> 5 M71T715_POS 70.98125 714.74066 0.05128205 0.12727273 0.02727273 <NA>
#> 6 M71T54_POS 71.04999 54.45641 0.15384615 0.99090909 0.05454545 <NA>
#> CAS.ID HMDB.ID KEGG.ID Lab.ID Adduct mz.error mz.match.score RT.error
#> 1 <NA> <NA> <NA> <NA> <NA> NA NA NA
#> 2 <NA> <NA> <NA> <NA> <NA> NA NA NA
#> 3 <NA> <NA> <NA> <NA> <NA> NA NA NA
#> 4 <NA> <NA> <NA> <NA> <NA> NA NA NA
#> 5 <NA> <NA> <NA> <NA> <NA> NA NA NA
#> 6 <NA> <NA> <NA> <NA> <NA> NA NA NA
#> RT.match.score CE SS Total.score Database Level
#> 1 NA <NA> NA NA <NA> NA
#> 2 NA <NA> NA NA <NA> NA
#> 3 NA <NA> NA NA <NA> NA
#> 4 NA <NA> NA NA <NA> NA
#> 5 NA <NA> NA NA <NA> NA
#> 6 NA <NA> NA NA <NA> NA
table(variable_info_pos$Level)
#>
#> 1 2
#> 23 183
table(variable_info_pos$Database)
#>
#> MoNA_0.0.1 MS_0.0.2 NIST_0.0.1
#> 78 23 105
Use the ms2_plot_mass_dataset() function to get the MS2 matching plot.
ms2_plot_mass_dataset(object = object_pos2,
variable_id = "M86T95_POS",
database = mona_database0.0.3)
#> $M86T95_POS_1
#>
#> $M86T95_POS_2
#>
#> $M86T95_POS_3
ms2_plot_mass_dataset(object = object_pos2,
variable_id = "M86T95_POS",
database = mona_database0.0.3,
interactive_plot = TRUE)
#> $M86T95_POS_1
#>
#> $M86T95_POS_2
#>
#> $M86T95_POS_3
ms2_plot_mass_dataset(object = object_pos2,
variable_id = "M147T54_POS",
database = snyder_database_rplc0.0.3,
interactive_plot = FALSE)
#> database may be wrong.
#> database may be wrong.
#> $M147T54_POS_1
#>
#> $M147T54_POS_2
#>
#> $M147T54_POS_3
Save data for subsequent analysis.
save(object_pos2, file = "metabolite_annotation/object_pos2")
save(object_neg2, file = "metabolite_annotation/object_neg2")
Session information
sessionInfo()
#> R version 4.1.2 (2021-11-01)
#> Platform: x86_64-apple-darwin17.0 (64-bit)
#> Running under: macOS Big Sur 10.16
#>
#> Matrix products: default
#> BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.1/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] stats4 stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] dplyr_1.0.8 metid_1.2.4 metpath_0.99.4
#> [4] massstat_0.99.13 ggfortify_0.4.14 massqc_0.99.7
#> [7] masscleaner_0.99.7 xcms_3.16.1 MSnbase_2.20.4
#> [10] ProtGenerics_1.26.0 S4Vectors_0.32.3 mzR_2.28.0
#> [13] Rcpp_1.0.8 Biobase_2.54.0 BiocGenerics_0.40.0
#> [16] BiocParallel_1.28.3 massprocesser_0.99.3 ggplot2_3.3.5
#> [19] masstools_0.99.5 massdataset_0.99.20 tidymass_0.99.6
#> [22] magrittr_2.0.2
#>
#> loaded via a namespace (and not attached):
#> [1] blogdown_1.7 tidyr_1.2.0
#> [3] missForest_1.4 knitr_1.37
#> [5] DelayedArray_0.20.0 data.table_1.14.2
#> [7] rpart_4.1.16 KEGGREST_1.34.0
#> [9] RCurl_1.98-1.5 doParallel_1.0.17
#> [11] generics_0.1.2 snow_0.4-4
#> [13] leaflet_2.1.0 preprocessCore_1.56.0
#> [15] mixOmics_6.18.1 RANN_2.6.1
#> [17] proxy_0.4-26 future_1.23.0
#> [19] tzdb_0.2.0 xml2_1.3.3
#> [21] lubridate_1.8.0 ggsci_2.9
#> [23] SummarizedExperiment_1.24.0 assertthat_0.2.1
#> [25] tidyverse_1.3.1 viridis_0.6.2
#> [27] xfun_0.29 hms_1.1.1
#> [29] jquerylib_0.1.4 evaluate_0.15
#> [31] DEoptimR_1.0-10 fansi_1.0.2
#> [33] dbplyr_2.1.1 readxl_1.3.1
#> [35] igraph_1.2.11 DBI_1.1.2
#> [37] htmlwidgets_1.5.4 MsFeatures_1.3.0
#> [39] rARPACK_0.11-0 purrr_0.3.4
#> [41] ellipsis_0.3.2 RSpectra_0.16-0
#> [43] crosstalk_1.2.0 backports_1.4.1
#> [45] bookdown_0.24 ggcorrplot_0.1.3
#> [47] MatrixGenerics_1.6.0 vctrs_0.3.8
#> [49] remotes_2.4.2 here_1.0.1
#> [51] withr_2.4.3 ggforce_0.3.3
#> [53] itertools_0.1-3 robustbase_0.93-9
#> [55] checkmate_2.0.0 svglite_2.0.0
#> [57] cluster_2.1.2 lazyeval_0.2.2
#> [59] crayon_1.5.0 ellipse_0.4.2
#> [61] labeling_0.4.2 pkgconfig_2.0.3
#> [63] tweenr_1.0.2 GenomeInfoDb_1.30.0
#> [65] nnet_7.3-17 rlang_1.0.1
#> [67] globals_0.14.0 lifecycle_1.0.1
#> [69] affyio_1.64.0 extrafontdb_1.0
#> [71] fastDummies_1.6.3 MassSpecWavelet_1.60.0
#> [73] modelr_0.1.8 cellranger_1.1.0
#> [75] randomForest_4.7-1 rprojroot_2.0.2
#> [77] polyclip_1.10-0 matrixStats_0.61.0
#> [79] Matrix_1.4-0 reprex_2.0.1
#> [81] base64enc_0.1-3 GlobalOptions_0.1.2
#> [83] png_0.1-7 viridisLite_0.4.0
#> [85] rjson_0.2.21 clisymbols_1.2.0
#> [87] bitops_1.0-7 pander_0.6.4
#> [89] Biostrings_2.62.0 shape_1.4.6
#> [91] stringr_1.4.0 parallelly_1.30.0
#> [93] robust_0.7-0 readr_2.1.2
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