1 Overview

The nmrdata package provides example one-dimensional proton NMR spectra of murine urine samples collected in a Roux-en-Y gastric bypass study (Li et al. 2011).

Data resources are hosted on Bioconductor’s ExperimentHub (EH) and retrieved on demand; this vignette describes data resources and illustrates retrieval.

Two resources are available:

  • a pre-processed dataset for quick example analyses (EH9905)
  • raw Bruker experiment folders for I/O demonstrations (EH9906);

These resources are intended for teaching and for demonstrating the companion package metabom8.

# checking metadata
meta_path <- system.file("extdata", "metadata.csv", package = "nmrdata")
if (file.exists(meta_path)) {
  meta <- utils::read.csv(meta_path)
  head(meta[c("Title","RDataPath")])
}
##                             Title                                RDataPath
## 1         Bariatric pre-processed   records/17053134/files/bariatric.rdata
## 2 Raw Bruker experiments (tar.gz) records/17053118/files/bruker_exp.tar.gz

2 Resource Description

  • Data were acquired on a Bruker Avance III 600 MHz spectrometer
  • Bariatric pre-processed (EH9905)
    • 1D NMR spectra acquired from rat urine specimens (n=67)
    • Baseline corrected, res. water signal excised, dilution normalised (probabilistic quotient normalisation)
    • Provided as named list with elements:
      • X.pqn: matrix of PQN-processed spectra
      • ppm: chemical shift axis (parts per million)
      • an: sample annotation (e.g., class membership)
      • meta: acquisition/processing status parameters (TopSpin; a_*, p_*)
  • Raw Bruker experiments (EH9906)
    • Minimal file set of raw Bruker proton NMR experiments (n=21)
    • Retain original files in Bruker directory structure (fid, acqus/procs, 1i/1r)
    • Accessed via getRawExpDir(); unpacked into the local EH cache on first use
    • Intended for I/O workflow demos (e.g., with the companion package metabom8)

3 Installation

# Install from Bioconductor
# if (!require("BiocManager")) install.packages("BiocManager")
# BiocManager::install("nmrdata")

4 Resource discovery via ExperimentHub

library(ExperimentHub)

eh <- ExperimentHub()
query(eh, "nmrdata")
## ExperimentHub with 2 records
## # snapshotDate(): 2026-04-13
## # $dataprovider: Imperial College London
## # $species: Rattus norvegicus
## # $rdataclass: list, character
## # additional mcols(): taxonomyid, genome, description,
## #   coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
## #   rdatapath, sourceurl, sourcetype 
## # retrieve records with, e.g., 'object[["EH9905"]]' 
## 
##            title                          
##   EH9905 | Bariatric pre-processed        
##   EH9906 | Raw Bruker experiments (tar.gz)

5 Importing Bariatric pre-processed data

hub_id = 'EH9905' # Bariatric pre-processed        

bariatric <- eh[[hub_id]]
str(bariatric, max.level = 1)
## List of 4
##  $ X.pqn: num [1:67, 1:56357] 4422 10654 7028 3707 3232 ...
##   ..- attr(*, "dimnames")=List of 2
##  $ ppm  : num [1:56357] 9.5 9.5 9.5 9.5 9.5 ...
##  $ an   :'data.frame':   67 obs. of  4 variables:
##  $ meta :'data.frame':   67 obs. of  417 variables:
# visualise the first NMR spectrum
plot(bariatric$ppm, bariatric$X.pqn[1, ], type = "l",
     xlab = "Chemical shift (ppm)", ylab = "Intensity")

# an: sample annotation data (row-matched to `X.pqn`)
head(bariatric$an)
##   ID  Class Timepoint NMR experiment
## 1 21 Pre-op       Pre            301
## 2 27 Pre-op       Pre            302
## 5 20   RYGB        W2            305
## 6 34   RYGB        W2            306
## 7 36 Pre-op       Pre            307
## 9 33 Pre-op       Pre            309
stopifnot(nrow(bariatric$an)==nrow(bariatric$X.pqn))

# meta: TopSpin acquisition and processing parameters (row-matched to `X.pqn`)
head(colnames(bariatric$meta), 10)
##  [1] "a_AQSEQ"   "a_AQ_mod"  "a_AUNM"    "a_AUTOPOS" "a_BF1"     "a_BF2"    
##  [7] "a_BF3"     "a_BF4"     "a_BF5"     "a_BF6"
stopifnot(nrow(bariatric$meta)==nrow(bariatric$X.pqn))

# Ex Parameters:
meta = bariatric$meta

meta$a_SFO1[1] # carrier frequency
## [1] 600.2928
meta$a_NS[1] # number of scans
## [1] 128
meta$a_OVERFLW[1] # overflow
## [1] 0
meta$p_SI[1] # nb of points in spectrum (zero filled)
## [1] 32768
meta$p_LB[1] # line broadening factor
## [1] 0.3

6 Importing raw Bruker experiments

library(nmrdata)

# download once, unpack once; returns the directory path
exp_dir <- getRawExpDir(quiet = TRUE)
# show experiment folder content
list.files(exp_dir, recursive = TRUE)[1:10]
##  [1] "bruker_exp/1/acqu"          "bruker_exp/1/acqus"        
##  [3] "bruker_exp/1/fid"           "bruker_exp/1/pdata/1/1i"   
##  [5] "bruker_exp/1/pdata/1/1r"    "bruker_exp/1/pdata/1/proc" 
##  [7] "bruker_exp/1/pdata/1/procs" "bruker_exp/101/acqu"       
##  [9] "bruker_exp/101/acqus"       "bruker_exp/101/fid"

If you have metabom8 installed, you can import/process the raw experiments:

library(metabom8)

# import Bruker 1D NMR spectra
res <- read1d_proc(exp_dir, exp_type = list(pulprog = "noesypr1d"))

# plot first spectrum
spec(res$X[1, ], res$ppm)

7 Caching and reproducibility

Data are cached under a per-user directory so repeated calls don’t re-download:

# where the archive and unpacked folder live
dir <- getRawExpDir(quiet = TRUE)
print(dir)
## [1] "/home/biocbuild/.cache/R/ExperimentHub/bruker_exp"

8 Package version

packageVersion("nmrdata")
## [1] '1.1.0'

9 Session info

sessionInfo()
## R version 4.6.0 RC (2026-04-17 r89917)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.4 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.24-bioc/R/lib/libRblas.so 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0  LAPACK version 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] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] nmrdata_1.1.0       ExperimentHub_3.1.0 AnnotationHub_4.1.0
## [4] BiocFileCache_3.1.0 dbplyr_2.5.2        BiocGenerics_0.57.1
## [7] generics_0.1.4      BiocStyle_2.39.0   
## 
## loaded via a namespace (and not attached):
##  [1] rappdirs_0.3.4       sass_0.4.10          BiocVersion_3.23.1  
##  [4] RSQLite_2.4.6        digest_0.6.39        magrittr_2.0.5      
##  [7] evaluate_1.0.5       bookdown_0.46        fastmap_1.2.0       
## [10] blob_1.3.0           jsonlite_2.0.0       AnnotationDbi_1.73.1
## [13] DBI_1.3.0            tinytex_0.59         BiocManager_1.30.27 
## [16] httr_1.4.8           purrr_1.2.2          Biostrings_2.79.5   
## [19] httr2_1.2.2          jquerylib_0.1.4      cli_3.6.6           
## [22] crayon_1.5.3         rlang_1.2.0          XVector_0.51.0      
## [25] Biobase_2.71.0       bit64_4.6.0-1        withr_3.0.2         
## [28] cachem_1.1.0         yaml_2.3.12          otel_0.2.0          
## [31] tools_4.6.0          memoise_2.0.1        dplyr_1.2.1         
## [34] filelock_1.0.3       curl_7.0.0           vctrs_0.7.3         
## [37] R6_2.6.1             png_0.1-9            magick_2.9.1        
## [40] stats4_4.6.0         lifecycle_1.0.5      Seqinfo_1.1.0       
## [43] KEGGREST_1.51.1      S4Vectors_0.49.2     IRanges_2.45.0      
## [46] bit_4.6.0            pkgconfig_2.0.3      pillar_1.11.1       
## [49] bslib_0.10.0         Rcpp_1.1.1-1         glue_1.8.1          
## [52] xfun_0.57            tibble_3.3.1         tidyselect_1.2.1    
## [55] knitr_1.51           htmltools_0.5.9      rmarkdown_2.31      
## [58] compiler_4.6.0

Li, Jia V, Hutan Ashrafian, Marco Bueter, James Kinross, Charles Sands, Carel W le Roux, Stephen R Bloom, et al. 2011. “Metabolic Surgery Profoundly Influences Gut Microbial-Host Metabolic Cross-Talk.” Gut 60 (9): 1214–23.