This user’s guide provides an overview of the package
ASICS is a
fully automated procedure to identify and quantify metabolites in \(^1\)H 1D-NMR
spectra of biological mixtures (Tardivel et al., 2017). It will enable
empowering NMR-based metabolomics by quickly and accurately helping experts to
obtain metabolic profiles. In addition to the quantification method, several
functions allowing spectrum preprocessing or statistical analyses of quantified
metabolites are available.
In this user’s guide, a subset of the public datasets from Salek et al. (2007) is used. The experiment has been designed to improve the understanding of early stage of type 2 diabetes mellitus (T2DM) development. In the dataset used, \(^1\)H-NMR human metabolome was obtained from 25 healthy volunteers and 25 T2DM patients. Raw 1D Bruker spectral data files were found in the MetaboLights database (https://www.ebi.ac.uk/metabolights/, study MTBLS1).
For most time consumming functions, a parallel implementation is available for
unix-like OS using the BiocParallel package of Bioconductor. The number of used
cores is set with the option
ncores of the corresponding functions (default
1, no parallel environment).
An object of class
PureLibrary with spectra of pure metabolites is required to
perform the quantification. Such a reference library is provided in
191 pure metabolite spectra. These spectra are metabolite spectra used as
references for quantification: only metabolites that are present in the library
object can be identified and quantified with
The default library is automatically loaded at package start. Available metabolites are displayed with:
head(getSampleName(pure_library), n = 8)
##  "1,3-Diaminopropane" "Levoglucosan" "1-Methylhydantoin" ##  "1-Methyl-L-Histidine" "QuinolinicAcid" "2-AminoAdipicAcid" ##  "2-AminobutyricAcid" "2-Deoxyadenosine"
This library can be complemented or another library can be created with new spectra of pure metabolites. These spectra are imported from Bruker files and a new library can be created with:
pure_spectra <- importSpectraBruker(system.file("extdata", "example_library", package = "ASICS")) new_pure_library <- createPureLibrary(pure_spectra, nb.protons = c(5, 4))
A new library can also be created from txt or csv files, with samples in columns
and chemical shifts in rows (see help page of
createPureLibrary function for
The newly created library can be used for quantification or merged with another one:
merged_pure_library <- c(pure_library[1:10], new_pure_library)
merged_pure_library contains the first ten spectra of the
default library and the two newly imported spectra.
First, data are imported in a data frame from Bruker files with the
importSpectraBruker function. These spectra are baseline corrected
(Wang et al, 2013) and normalised by the area under the curve.
spectra_data <- importSpectraBruker(system.file("extdata", "Human_diabetes_example", package = "ASICSdata"))
Data can also be imported from other file types with
The only constraint is to have a data frame with spectra in columns
(column names are sample names) and chemical shifts in rows (row names
correspond to the ppm grid).
diabetes <- system.file("extdata", package = "ASICSdata") spectra_data_txt <- importSpectra(name.dir = diabetes, name.file = "spectra_diabetes_example.txt", type = "txt")
Several functions for the preprocessing of spectra are also available: normalisation and alignment on a reference spectrum (based on Vu et al. (2011)).
Many types of normalisation are available. By default, spectra are normalised
to a constant sum (
type.norm = "CS"). Otherwise, a normalisation method
implemented in the
PepsNMR package could be used. For example:
spectra_norm <- normaliseSpectra(spectra_data_txt, type.norm = "pqn")
## Normalisation method : pqn
The alignment algorithm is based on Vu et al. (2011). To find the reference spectrum, the FFT cross-correlation is used. Then the alignment is performed using the FFT cross-correlation and a hierarchical classification.
spectra_align <- alignSpectra(spectra_norm)
Finally, from the data frame, a
Spectra object is created. This is a required
step for the quantification.
spectra_obj <- createSpectra(spectra_norm)
Identification and quantification of metabolites can now be carried out using
only the function
ASICS. All the steps described in the following figure are
Recently, new methods for reference library alignment and metabolite quantification were added. Thus, multiple scenarios can be performed:
The method provided in the first version of the package is given in red. It can
now be used by setting
joint.align = FALSE and
quantif.method = "FWER". To
perform a joint alignment (blue, green and yellow scenarios),
needs to be set to
TRUE. The yellow scenario that performs joint
quantification based on a simple joint alignment is obtained by additionally
quantif.method = "Lasso". Finally, the green scenario performs a joint
quantification using metabolites identified with a first step consisting of
independent quantification. It is obtained by setting
quantif.method = "both".
quantif.method = "both", the number of identified metabolites can be
clean.thres = 10, only the metabolites
identified in at least 10% of the complex spectra (during the first independant
quantification step) are used in the joint quantification.
More details on these new algorithms can be found in Lefort et al. (2020).
ASICS function takes approximately 2 minutes per
spectrum to run. To control randomness in the algorithm (used in the estimation
of the significativity of a given metabolite concentration), the
parameter can be used.
# part of the spectrum to exclude (water and urea) to_exclude <- matrix(c(4.5, 5.1, 5.5, 6.5), ncol = 2, byrow = TRUE) ASICS_results <- ASICS(spectra_obj, exclusion.areas = to_exclude)
Summary of ASICS results:
## An object of class ASICSResults ## It contains 50 spectra of 31087 points. ## ## ASICS results: ## 162 metabolites are identified for this set of spectra. ## Most concentrated metabolites are: Creatinine, Citrate, AceticAcid, L-GlutamicAcid, L-Glycine, L-Proline
The quality of the results can be assessed by stacking the original and the reconstructed spectra on one plot. A pure metabolite spectrum can also be added for visual comparison. For example, the first spectrum with Creatinine:
plot(ASICS_results, idx = 1, xlim = c(2.8, 3.3), add.metab = "Creatinine")