| Title: | Example of 1D NMR spectra data for ASICS package |
|---|---|
| Description: | 1D NMR example spectra and additional data for use with the ASICS package. Raw 1D Bruker spectral data files were found in the MetaboLights database (https://www.ebi.ac.uk/metabolights/, study MTBLS1). |
| Authors: | Gaëlle Lefort [aut, cre], Rémi Servien [aut], Nathalie Villa-Vialaneix [aut] |
| Maintainer: | Gaëlle Lefort <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.32.0 |
| Built: | 2026-10-10 11:10:42 UTC |
| Source: | https://github.com/bioc/ASICSdata |
File containing the design of experiment of Salek et al. (2007) study.
txt file, separated by spaces, with two columns (sample name and two level condition: control/diabete)
https://www.ebi.ac.uk/metabolights/MTBLS1
Salek R.M., Maguire M.L., Bentley E., Rubtsov D.V., Hough T., Cheeseman M., Nunez D., Sweatman B.C., Haselden J.N., Cox R.D., Connor S.C., Griffin J.L. (2007). A metabolomic comparison of urinary changes in type 2 diabetes in mouse, rat, and human. Physiological genomics, 29(2), 99-108. DOI: 10.1152/physiolgenomics.00194.2006
design_path <- system.file("extdata", "design_diabete_example.txt", package = "ASICSdata") design <- read.table(design_path, stringsAsFactor = FALSE, header = TRUE) head(design)design_path <- system.file("extdata", "design_diabete_example.txt", package = "ASICSdata") design <- read.table(design_path, stringsAsFactor = FALSE, header = TRUE) head(design)
This dataset is a subset of the public datasets from Salek et al. (2007).
The experiment has been designed to improve the understanding of early
stage of type 2 diabetes mellitus (T2DM) development. $^1$H-NMR human
metabolome was obtained from 25 healthy volunteers and 25 T2DM patients.
Raw 1D Bruker spectra were obtained from the MetaboLights database
(https://www.ebi.ac.uk/metabolights/, study MTBLS1).
The dataset contains 50 spectra (columns in TXT file) and 31087 chemical
shifts (rows in TXT file). Row names in the TXT file indicate the chemical
shifts in ppm.
Raw 1D spectra (Brucker or txt formats)
https://www.ebi.ac.uk/metabolights/MTBLS1
Salek R.M., Maguire M.L., Bentley E., Rubtsov D.V., Hough T., Cheeseman M., Nunez D., Sweatman B.C., Haselden J.N., Cox R.D., Connor S.C., Griffin J.L. (2007). A metabolomic comparison of urinary changes in type 2 diabetes in mouse, rat, and human. Physiological genomics, 29(2), 99-108. DOI: 10.1152/physiolgenomics.00194.2006
# Raw 1D Bruker spectra data_path <- system.file("extdata", "Human_diabetes_example", package = "ASICSdata") dir(data_path) # txt file data_path <- system.file("extdata", "spectra_diabetes_example.txt", package = "ASICSdata") spectra <- read.table(data_path, header = TRUE, row.names = 1) dim(spectra)# Raw 1D Bruker spectra data_path <- system.file("extdata", "Human_diabetes_example", package = "ASICSdata") dir(data_path) # txt file data_path <- system.file("extdata", "spectra_diabetes_example.txt", package = "ASICSdata") spectra <- read.table(data_path, header = TRUE, row.names = 1) dim(spectra)
Results of ASICS (Tardivel et al., 2017) for metabolite quantification on spectra from Salek et al. (2007)..
txt file with 150 rows (quantified metabolites) and 50 columns (spectra), separated by spaces with row names (metabolite names) and column names (sample identifiers)
Salek R.M., Maguire M.L., Bentley E., Rubtsov D.V., Hough T., Cheeseman M., Nunez D., Sweatman B.C., Haselden J.N., Cox R.D., Connor S.C., Griffin J.L. (2007). A metabolomic comparison of urinary changes in type 2 diabetes in mouse, rat, and human. Physiological genomics, 29(2), 99-108. DOI: 10.1152/physiolgenomics.00194.2006
Tardivel P., Canlet C., Lefort G., Tremblay-Franco M., Debrauwer L., Concordet D., Servien R. (2017). ASICS: an automatic method for identification and quantification of metabolites in complex 1D 1H NMR spectra. Metabolomics, 13(10), 109. DOI: 10.1007/s11306-017-1244-5
data_path <- system.file("extdata", "results_ASICS.txt", package = "ASICSdata") results <- read.table(data_path, header = TRUE, row.names = 1) dim(results)data_path <- system.file("extdata", "results_ASICS.txt", package = "ASICSdata") results <- read.table(data_path, header = TRUE, row.names = 1) dim(results)