ASICS User’s Guide
2020-11-23
This user’s guide provides an overview of the package ASICS. 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.
library(ASICS)
library(ASICSdata)1 Dataset
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).
2 Parallel environment
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
to 1, no parallel environment).
3 Library of pure NMR metabolite spectrum
An object of class PureLibrary with spectra of pure metabolites is required to
perform the quantification. Such a reference library is provided in ASICS with
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 ASICS.
The default library is automatically loaded at package start. Available metabolites are displayed with:
head(getSampleName(pure_library), n = 8)## [1] "1,3-Diaminopropane" "Levoglucosan" "1-Methylhydantoin"
## [4] "1-Methyl-L-Histidine" "QuinolinicAcid" "2-AminoAdipicAcid"
## [7] "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
all details).
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)The PureLibrary merged_pure_library contains the first ten spectra of the
default library and the two newly imported spectra.
4 Identification and quantification of metabolites with ASICS
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 importSpectra function.
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 : pqnThe 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_align)Identification and quantification of metabolites can now be carried out using
only the function ASICS. All the steps described in the following figure are
included:
Steps of the quantification workflow
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), joint.align
needs to be set to TRUE. The yellow scenario that performs joint
quantification based on a simple joint alignment is obtained by additionally
setting 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".
With quantif.method = "both", the number of identified metabolites can be
controlled using clean.thres. If 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 set.seed
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:
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-ProlineThe 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")
Relative concentrations of identified metabolites are saved in a data frame
accessible via the get_quantification function:
head(getQuantification(ASICS_results), 10)[, 1:2]## ADG10003u_007 ADG10003u_008
## Creatinine 0.027524645 0.016992328
## Citrate 0.009818148 0.003665571
## AceticAcid 0.004454905 0.000000000
## L-GlutamicAcid 0.002655211 0.001562384
## L-Glycine 0.002147344 0.002070631
## L-Proline 0.001960824 0.003139267
## 2-AminoAdipicAcid 0.001943956 0.001038924
## D-Mannose 0.001916896 0.002843512
## L-Aspartate 0.001868333 0.004047524
## ThreonicAcid 0.001803600 0.0000000005 Analysis on relative quantifications
Some analysis functions are also available in ASICS.
First, a design data frame is imported. In this data frame, the first column needs to correspond to sample names of all spectra.
design <- read.table(system.file("extdata", "design_diabete_example.txt",
package = "ASICSdata"), header = TRUE)Then, a preprocessing is performed on relative quantifications: metabolites with more than 75% of null quantifications are removed as well as two samples that are considered as outliers.
analysis_data <- formatForAnalysis(getQuantification(ASICS_results),
design = design, zero.threshold = 75,
zero.group = "condition",
outliers = c("ADG10003u_007",
"ADG19007u_163"))To explore results of ASICS quantification, a PCA can be performed on results of preprocessing with:
resPCA <- pca(analysis_data)## Warning: 'info.txtC = NULL' argument value is deprecated; use 'info.txtC =
## 'none'' instead.## Warning: 'fig.pdfC = NULL' argument value is deprecated; use 'fig.pdfC = 'none''
## instead.plot(resPCA, graph = "ind", col.ind = "condition")
plot(resPCA, graph = "var")
It is also possible to find differences between two conditions with an OPLS-DA (Thevenot et al, 2015) or with Kruskall-Wallis tests:
resOPLSDA <- oplsda(analysis_data, condition = "condition", orthoI = 1)
resOPLSDA## OPLS-DA performed on quantifications
## Cross validation error: 0.12
##
## Variable with the higher VIP:
## Control Group diabetes mellitus VIP influential
## L-Citrulline 1.577558e-03 6.348160e-04 2.293155 TRUE
## Galactitol 1.062704e-03 2.947166e-04 2.259247 TRUE
## D-Glucose-6-Phosphate 1.718698e-03 2.042602e-03 2.046632 TRUE
## Trimethylamine 0.000000e+00 3.390938e-05 1.885807 TRUE
## Uracil 1.290575e-03 4.742019e-04 1.862658 TRUE
## 3-PhenylPropionicAcid 6.515734e-04 8.718475e-04 1.818105 TRUE
## D-GluconicAcid 7.105618e-04 1.667304e-03 1.797962 TRUE
## 2-Oxobutyrate 4.440598e-05 5.164097e-04 1.754901 TRUE
## UrocanicAcid 1.578936e-05 1.932797e-04 1.713738 TRUE
## Levoglucosan 7.772079e-04 3.861891e-04 1.683468 TRUE
## [...]plot(resOPLSDA)
Results of Kruskall-Wallis tests and Benjamini-Hochberg correction:
resTests <- kruskalWallis(analysis_data, "condition")
resTests## Kruskal-Wallis tests performed on quantifications
## Variable with the lower adjusted p-value:
##
## Feature Adjusted.p.value
## 1 L-Citrulline 9.789748e-05
## 2 Galactitol 2.703854e-04
## 3 Glycerol 3.268214e-03
## 4 Ethanolamine 1.624328e-02
## 5 Uracil 6.168396e-02
## 6 D-Glucose-6-Phosphate 6.568338e-02
## 7 D-GluconicAcid 9.634158e-02
## 8 Inosine 1.117938e-01
## 9 Trigonelline 1.148400e-01
## 10 2-Oxobutyrate 1.617243e-01
## [...]plot(resTests)
6 Analysis on buckets
An analysis on buckets can also be performed. An alignment is required before the spectrum bucketing:
spectra_align <- alignSpectra(spectra_norm)
spectra_bucket <- binning(spectra_align)Alignment visualization:
spectra_obj_align <- createSpectra(spectra_align)
plotAlignment(spectra_obj, xlim = c(3.5,4))
plotAlignment(spectra_obj_align, xlim = c(3.5,4))
Then, a SummarizedExperiment object is created with the formatForAnalysis
function as for quantification:
analysis_data_bucket <- formatForAnalysis(spectra_bucket, design = design,
zero.threshold = 75)Finally, all analyses can be carried out on this object with the parameter
type.data set to buckets. For example, the OPLS-DA is performed with:
resOPLSDA_buckets <- oplsda(analysis_data_bucket, condition = "condition",
type.data = "buckets")
resOPLSDA_buckets## OPLS-DA performed on buckets
## Cross validation error: 0.12
##
## Variable with the higher VIP:
## Control Group diabetes mellitus VIP influential
## 8.935 -1.133369e-05 3.615169e-07 2.240782 TRUE
## 4.115 1.014203e-03 1.317695e-03 2.164346 TRUE
## 3.785 4.080651e-03 4.902450e-03 2.144845 TRUE
## 8.625 -1.267405e-05 6.430714e-06 2.093072 TRUE
## 3.685 8.273601e-03 5.333275e-03 2.090887 TRUE
## 4.245 7.322459e-04 4.692211e-04 2.074871 TRUE
## 9.735 -8.501084e-06 2.194731e-06 2.063978 TRUE
## 6.335 1.075097e-05 3.006047e-05 2.036788 TRUE
## 5.255 2.055731e-04 3.849152e-04 2.032645 TRUE
## 8.635 -7.321572e-06 7.141929e-06 1.968756 TRUE
## [...]Moreover, another plot with the median spectrum and OPLS-DA results can be
produced with the option graph = "buckets":
plot(resOPLSDA_buckets, graph = "buckets")
7 References
Lefort G., Liaubet L., Marty-Gasset N., Canlet C., Vialaneix N., Servien R. . 2020. Pre-print, https://www.biorxiv.org/content/10.1101/2020.10.08.331090v1.
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. https://doi.org/10.1007/s11306-017-1244-5
Salek, R. M., Maguire, M. L., Bentley, E., Rubtsov, D. V., Hough, T., Cheeseman, M., … & Connor, S. C. (2007). A metabolomic comparison of urinary changes in type 2 diabetes in mouse, rat, and human. Physiological genomics, 29(2), 99-108.
Wang, K. C., Wang, S. Y., Kuo, C. H., Tseng, Y. J. (2013). Distribution-based classification method for baseline correction of metabolomic 1D proton nuclear magnetic resonance spectra. Analytical Chemistry, 85(2), 1231–1239.
Vu, T. N., Valkenborg, D., Smets, K., Verwaest, K. A., Dommisse, R., Lemiere, F., … & Laukens, K. (2011). An integrated workflow for robust alignment and simplified quantitative analysis of NMR spectrometry data. BMC bioinformatics, 12(1), 405.
Thevenot, E.A., Roux, A., Xu, Y., Ezan, E., Junot, C. 2015. Analysis of the human adult urinary metabolome variations with age, body mass index and gender by implementing a comprehensive workflow for univariate and OPLS statistical analyses. Journal of Proteome Research. 14, 3322-3335.
8 Session information
This user’s guide has been created with the following system configuration:
sessionInfo()## R version 4.0.3 (2020-10-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.4 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
##
## locale:
## [1] LC_CTYPE=fr_FR.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=fr_FR.UTF-8 LC_COLLATE=fr_FR.UTF-8
## [5] LC_MONETARY=fr_FR.UTF-8 LC_MESSAGES=fr_FR.UTF-8
## [7] LC_PAPER=fr_FR.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=fr_FR.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ASICSdata_1.8.0 ASICS_2.6.1 BiocStyle_2.16.1
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.5 mvtnorm_1.1-1
## [3] lattice_0.20-41 zoo_1.8-8
## [5] ropls_1.20.0 glmnet_4.0-2
## [7] digest_0.6.27 foreach_1.5.1
## [9] R6_2.5.0 GenomeInfoDb_1.24.2
## [11] plyr_1.8.6 stats4_4.0.3
## [13] evaluate_0.14 ggplot2_3.3.2
## [15] pillar_1.4.6 zlibbioc_1.34.0
## [17] rlang_0.4.8 nloptr_1.2.2.2
## [19] S4Vectors_0.26.1 Matrix_1.2-18
## [21] rmarkdown_2.5 labeling_0.4.2
## [23] splines_4.0.3 BiocParallel_1.22.0
## [25] stringr_1.4.0 RCurl_1.98-1.2
## [27] munsell_0.5.0 DelayedArray_0.14.1
## [29] compiler_4.0.3 xfun_0.18
## [31] pkgconfig_2.0.3 BiocGenerics_0.34.0
## [33] shape_1.4.5 htmltools_0.5.0
## [35] tidyselect_1.1.0 SummarizedExperiment_1.18.2
## [37] tibble_3.0.4 gridExtra_2.3
## [39] GenomeInfoDbData_1.2.3 bookdown_0.21
## [41] quadprog_1.5-8 IRanges_2.22.2
## [43] codetools_0.2-16 matrixStats_0.57.0
## [45] crayon_1.3.4 dplyr_1.0.2
## [47] MASS_7.3-53 bitops_1.0-6
## [49] grid_4.0.3 gtable_0.3.0
## [51] lifecycle_0.2.0 magrittr_1.5
## [53] PepsNMR_1.6.1 scales_1.1.1
## [55] stringi_1.5.3 farver_2.0.3
## [57] XVector_0.28.0 reshape2_1.4.4
## [59] ptw_1.9-15 ellipsis_0.3.1
## [61] generics_0.1.0 vctrs_0.3.4
## [63] RColorBrewer_1.1-2 iterators_1.0.13
## [65] tools_4.0.3 Biobase_2.48.0
## [67] glue_1.4.2 purrr_0.3.4
## [69] parallel_4.0.3 survival_3.2-7
## [71] yaml_2.2.1 colorspace_1.4-1
## [73] BiocManager_1.30.10 GenomicRanges_1.40.0
## [75] knitr_1.30