1 Introduction

The MsDataHub package provides example mass spectrometry data, peptide spectrum matches or quantitative data from proteomics and metabolomics experiments. The data are served through the ExperimentHub infrastructure, which allows download them only ones and cache them for further use. Currently available data are summarised in the table below and details in the next section.

library("MsDataHub")
DT::datatable(MsDataHub())

2 Installation

To install the package:

if (!require("BiocManager"))
    install.packages("BiocManager")

BiocManager::install("MsDataHub")

3 Available data

3.1 TripleTOF

Type: Raw MS data
Files: PestMix1_DDA.mzML and PestMix1_SWATH.mzML
More details: ?TripleTOF

Load with

f <- PestMix1_DDA.mzML()

## see ?MsDataHub and browseVignettes('MsDataHub') for documentation

## loading from cache

library(Spectra)
Spectra(f)

## MSn data (Spectra) with 7602 spectra in a MsBackendMzR backend:
##        msLevel     rtime scanIndex
##      <integer> <numeric> <integer>
## 1            1     0.231         1
## 2            1     0.351         2
## 3            1     0.471         3
## 4            1     0.591         4
## 5            1     0.711         5
## ...        ...       ...       ...
## 7598         1   899.491      7598
## 7599         1   899.613      7599
## 7600         1   899.747      7600
## 7601         1   899.872      7601
## 7602         1   899.993      7602
##  ... 33 more variables/columns.
## 
## file(s):
## 1e0e954878e0b6_7861

f <- PestMix1_SWATH.mzML()

## see ?MsDataHub and browseVignettes('MsDataHub') for documentation

## loading from cache

Spectra(f)

## MSn data (Spectra) with 8999 spectra in a MsBackendMzR backend:
##        msLevel     rtime scanIndex
##      <integer> <numeric> <integer>
## 1            2     0.203         1
## 2            2     0.300         2
## 3            2     0.397         3
## 4            2     0.494         4
## 5            2     0.591         5
## ...        ...       ...       ...
## 8995         2   899.527      8995
## 8996         2   899.624      8996
## 8997         2   899.721      8997
## 8998         2   899.818      8998
## 8999         2   899.915      8999
##  ... 33 more variables/columns.
## 
## file(s):
## 1e0e955f137cc1_7862

3.2 sciex

Type: Raw MS data
Files: 20171016_POOL_POS_1_105-134.mzML and 20171016_POOL_POS_3_105-134.mzML
More details: ?sciex

Load with

f <- X20171016_POOL_POS_1_105.134.mzML()

## see ?MsDataHub and browseVignettes('MsDataHub') for documentation

## loading from cache

Spectra(f)

## MSn data (Spectra) with 931 spectra in a MsBackendMzR backend:
##       msLevel     rtime scanIndex
##     <integer> <numeric> <integer>
## 1           1     0.280         1
## 2           1     0.559         2
## 3           1     0.838         3
## 4           1     1.117         4
## 5           1     1.396         5
## ...       ...       ...       ...
## 927         1   258.641       927
## 928         1   258.920       928
## 929         1   259.199       929
## 930         1   259.478       930
## 931         1   259.757       931
##  ... 33 more variables/columns.
## 
## file(s):
## 1e0e957f85077d_7859

f <- X20171016_POOL_POS_3_105.134.mzML()

## see ?MsDataHub and browseVignettes('MsDataHub') for documentation

## loading from cache

Spectra(f)

## MSn data (Spectra) with 931 spectra in a MsBackendMzR backend:
##       msLevel     rtime scanIndex
##     <integer> <numeric> <integer>
## 1           1     0.275         1
## 2           1     0.554         2
## 3           1     0.833         3
## 4           1     1.112         4
## 5           1     1.391         5
## ...       ...       ...       ...
## 927         1   258.636       927
## 928         1   258.915       928
## 929         1   259.194       929
## 930         1   259.473       930
## 931         1   259.752       931
##  ... 33 more variables/columns.
## 
## file(s):
## 1e0e9547eaa1e7_7860

3.3 PXD000001

Type: Raw MS data and peptide spectrum matches
Files: TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzML.gz and TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzid
More details: ?PDX000001

Load with

f <- TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.20141210.mzML.gz()

## see ?MsDataHub and browseVignettes('MsDataHub') for documentation

## loading from cache

Spectra(f)

## MSn data (Spectra) with 7534 spectra in a MsBackendMzR backend:
##        msLevel     rtime scanIndex
##      <integer> <numeric> <integer>
## 1            1    0.4584         1
## 2            1    0.9725         2
## 3            1    1.8524         3
## 4            1    2.7424         4
## 5            1    3.6124         5
## ...        ...       ...       ...
## 7530         2   3600.47      7530
## 7531         2   3600.83      7531
## 7532         2   3601.18      7532
## 7533         2   3601.57      7533
## 7534         2   3601.98      7534
##  ... 33 more variables/columns.
## 
## file(s):
## 1e0e95128647c4_7858

f <- TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.20141210.mzid()

## see ?MsDataHub and browseVignettes('MsDataHub') for documentation

## loading from cache

library(PSMatch)
PSM(f)

## PSM with 5802 rows and 35 columns.
## names(35): sequence spectrumID ... subReplacementResidue subLocation

3.4 CPTAC

Type: tab-delimited quantitative proteomics data tables (as produced by MaxQuant)
Files: cptac_a_b_c_peptides.txt, cptac_a_b_peptides.txt and cptac_peptides.txt
More details: ?cptac

Load with

library(QFeatures)
f <- cptac_peptides.txt()

## see ?MsDataHub and browseVignettes('MsDataHub') for documentation

## loading from cache

ecols <- grep("Intensity\\.", names(read.delim(f)))
readSummarizedExperiment(f, ecols, sep = "\t")

## class: SummarizedExperiment 
## dim: 11466 45 
## metadata(0):
## assays(1): ''
## rownames(11466): 1 2 ... 11465 11466
## rowData names(143): Sequence N.term.cleavage.window ...
##   Oxidation..M..site.IDs MS.MS.Count
## colnames(45): Intensity.6A_1 Intensity.6A_2 ... Intensity.6E_8
##   Intensity.6E_9
## colData names(0):

cptac_a_b_c_peptides.txt()

## see ?MsDataHub and browseVignettes('MsDataHub') for documentation

## loading from cache

##                                                       EH7804 
## "/home/biocbuild/.cache/R/ExperimentHub/1e0e95147925cd_7854"

cptac_a_b_peptides.txt()

## see ?MsDataHub and browseVignettes('MsDataHub') for documentation
## loading from cache

##                                                       EH7805 
## "/home/biocbuild/.cache/R/ExperimentHub/1e0e9541f34035_7855"

3.5 FAAH KO

Type: Raw MS data, in netCDF format.
File: ko15.CDF
More details: ?cdf

Load with

f <- ko15.CDF()

## see ?MsDataHub and browseVignettes('MsDataHub') for documentation

## loading from cache

Spectra(f)

## MSn data (Spectra) with 1278 spectra in a MsBackendMzR backend:
##        msLevel     rtime scanIndex
##      <integer> <numeric> <integer>
## 1            1   2501.38         1
## 2            1   2502.94         2
## 3            1   2504.51         3
## 4            1   2506.07         4
## 5            1   2507.64         5
## ...        ...       ...       ...
## 1274         1   4493.56      1274
## 1275         1   4495.13      1275
## 1276         1   4496.69      1276
## 1277         1   4498.26      1277
## 1278         1   4499.82      1278
##  ... 33 more variables/columns.
## 
## file(s):
## 1e0e951bb72970_7853

3.6 DIA-NN software outputs

Type: tab-delimited DIA quantitative proteomics data tables produced by DIA-NN.
Files:
- Label-free DIA: benchmarkingDIA.tsv
- mTRAQ plexDIA: Report.Derks2022.plexDIA.tsv
More details: ?benchmarkingDIA.tsv and ?Report.Derks2022.plexDIA.tsv

Load with

library(QFeatures)
lfdia <- read.delim(MsDataHub::benchmarkingDIA.tsv())
readQFeaturesFromDIANN(lfdia)

## An instance of class QFeatures containing 24 assays:
##  [1] U:\712006-Proteomics\Issues\Issue 253\DIANN\raw-data\RD139_Overlap_UPS1_0_1fmol_inj1.mzML: SummarizedExperiment with 28980 rows and 1 columns 
##  [2] U:\712006-Proteomics\Issues\Issue 253\DIANN\raw-data\RD139_Overlap_UPS1_0_1fmol_inj2.mzML: SummarizedExperiment with 29495 rows and 1 columns 
##  [3] U:\712006-Proteomics\Issues\Issue 253\DIANN\raw-data\RD139_Overlap_UPS1_0_1fmol_inj3.mzML: SummarizedExperiment with 29210 rows and 1 columns 
##  ...
##  [22] U:\712006-Proteomics\Issues\Issue 253\DIANN\raw-data\RD139_Overlap_UPS1_5fmol_inj1.mzML: SummarizedExperiment with 30941 rows and 1 columns 
##  [23] U:\712006-Proteomics\Issues\Issue 253\DIANN\raw-data\RD139_Overlap_UPS1_5fmol_inj2.mzML: SummarizedExperiment with 30321 rows and 1 columns 
##  [24] U:\712006-Proteomics\Issues\Issue 253\DIANN\raw-data\RD139_Overlap_UPS1_5fmol_inj3.mzML: SummarizedExperiment with 24168 rows and 1 columns

plexdia <- read.delim(MsDataHub::Report.Derks2022.plexDIA.tsv())
readQFeaturesFromDIANN(plexdia, multiplexing = "mTRAQ")

## An instance of class QFeatures containing 54 assays:
##  [1] F:\JD\plexDIA\nPOP\wJD1146.raw: SummarizedExperiment with 2635 rows and 3 columns 
##  [2] F:\JD\plexDIA\nPOP\wJD1147.raw: SummarizedExperiment with 3000 rows and 3 columns 
##  [3] F:\JD\plexDIA\nPOP\wJD1148.raw: SummarizedExperiment with 2676 rows and 3 columns 
##  ...
##  [52] F:\JD\plexDIA\nPOP\wJD1203.raw: SummarizedExperiment with 4441 rows and 3 columns 
##  [53] F:\JD\plexDIA\nPOP\wJD1204.raw: SummarizedExperiment with 4416 rows and 3 columns 
##  [54] F:\JD\plexDIA\nPOP\wJD1205.raw: SummarizedExperiment with 4492 rows and 3 columns

4 Adding data to `MsDataHub`

If you would like additional dataset to MsDataHub, start by opening an issue in the package’s GitHub repository and describe the new data. In particular, provide information about it’s provenance, its use, its format(s) and acknowledge that the data may be shared freely with the community without any restrictions. You may provide an open licence specifying the terms it can be re-used, typically a CC-BY-SA license.
By contribution to the package, you acknowledge that you will comply to the R for Mass Spectrometry project code of conduct.
A maintainer of the package will reply to your issue, confirming that the data can be added.
At this point, if you are familiar with the development of ExperimentHub packages and GitHub pull requests, you may directly send one that adds your data to the package. Make sure (1) add appropriate references in the manual page and (2) to add yourself as a contributor of the package in the DESCRIPTION file.
Alternatively, a maintainer will add the dataset to the package and may require your input to make sure the documentation file is complete.

Session information

## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.20-bioc/R/lib/libRblas.so 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB              LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: America/New_York
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] MsDataHub_1.6.0             QFeatures_1.16.0           
##  [3] MultiAssayExperiment_1.32.0 SummarizedExperiment_1.36.0
##  [5] Biobase_2.66.0              GenomicRanges_1.58.0       
##  [7] GenomeInfoDb_1.42.0         IRanges_2.40.0             
##  [9] MatrixGenerics_1.18.0       matrixStats_1.4.1          
## [11] PSMatch_1.10.0              Spectra_1.16.0             
## [13] BiocParallel_1.40.0         S4Vectors_0.44.0           
## [15] BiocGenerics_0.52.0         BiocStyle_2.34.0           
## 
## loaded via a namespace (and not attached):
##  [1] DBI_1.2.3               rlang_1.1.4             magrittr_2.0.3         
##  [4] clue_0.3-65             compiler_4.4.1          RSQLite_2.3.7          
##  [7] png_0.1-8               vctrs_0.6.5             reshape2_1.4.4         
## [10] stringr_1.5.1           ProtGenerics_1.38.0     pkgconfig_2.0.3        
## [13] MetaboCoreUtils_1.14.0  crayon_1.5.3            fastmap_1.2.0          
## [16] dbplyr_2.5.0            XVector_0.46.0          utf8_1.2.4             
## [19] rmarkdown_2.28          UCSC.utils_1.2.0        purrr_1.0.2            
## [22] bit_4.5.0               xfun_0.48               zlibbioc_1.52.0        
## [25] cachem_1.1.0            jsonlite_1.8.9          blob_1.2.4             
## [28] DelayedArray_0.32.0     parallel_4.4.1          cluster_2.1.6          
## [31] R6_2.5.1                bslib_0.8.0             stringi_1.8.4          
## [34] jquerylib_0.1.4         Rcpp_1.0.13             bookdown_0.41          
## [37] knitr_1.48              Matrix_1.7-1            igraph_2.1.1           
## [40] tidyselect_1.2.1        abind_1.4-8             yaml_2.3.10            
## [43] codetools_0.2-20        curl_5.2.3              lattice_0.22-6         
## [46] tibble_3.2.1            plyr_1.8.9              withr_3.0.2            
## [49] KEGGREST_1.46.0         evaluate_1.0.1          BiocFileCache_2.14.0   
## [52] ExperimentHub_2.14.0    Biostrings_2.74.0       pillar_1.9.0           
## [55] BiocManager_1.30.25     filelock_1.0.3          DT_0.33                
## [58] ncdf4_1.23              generics_0.1.3          BiocVersion_3.20.0     
## [61] glue_1.8.0              lazyeval_0.2.2          tools_4.4.1            
## [64] AnnotationHub_3.14.0    mzR_2.40.0              fs_1.6.4               
## [67] grid_4.4.1              tidyr_1.3.1             crosstalk_1.2.1        
## [70] MsCoreUtils_1.18.0      AnnotationDbi_1.68.0    GenomeInfoDbData_1.2.13
## [73] cli_3.6.3               rappdirs_0.3.3          fansi_1.0.6            
## [76] S4Arrays_1.6.0          dplyr_1.1.4             AnnotationFilter_1.30.0
## [79] sass_0.4.9              digest_0.6.37           SparseArray_1.6.0      
## [82] htmlwidgets_1.6.4       memoise_2.0.1           htmltools_0.5.8.1      
## [85] lifecycle_1.0.4         httr_1.4.7              mime_0.12              
## [88] bit64_4.5.2             MASS_7.3-61

Mass Spectrometry Data on ExperimentHub

29 October 2024

Package

1 Introduction

2 Installation

3 Available data

3.1 TripleTOF

3.2 sciex

3.3 PXD000001

3.4 CPTAC

3.5 FAAH KO

3.6 DIA-NN software outputs

4 Adding data to `MsDataHub`

Session information

Mass Spectrometry Data on ExperimentHub

29 October 2024

Package

1 Introduction

2 Installation

3 Available data

3.1 TripleTOF

3.2 sciex

3.3 PXD000001

3.4 CPTAC

3.5 FAAH KO

3.6 DIA-NN software outputs

4 Adding data to MsDataHub

Session information

4 Adding data to `MsDataHub`