For the metabolome data, all calculations and statistical analyses were performed using Python. The Shapiro-Wilk test was performed to identify the metabolites whose concentrations in the blood showed a normal distribution, and Student’s t-test was used to compare their concentrations in blood samples for the IUGR and NORM groups. Metabolites whose concentrations did not show a normal distribution were compared between the two groups using the non-parametric Mann–Whitney test. The Benjamini–Hochberg correction was applied in both cases to account for the risk I inflation associated with multiple comparisons. Before being subjected to unsupervised and supervised algorithms, the concentration of each metabolite was normalised and centred. Principal component analysis (PCA) and orthogonal projection to latent structures-discriminant analysis (OPLS-DA) were employed as unsupervised and supervised methods in the multivariate analysis, respectively. PCA was used for the identification of outliers (Mahalanobis distance metric) as well as the spontaneous clustering of similar samples in the scatter plot of the two principal components. In the OPLS-DA analysis, the X matrix consisted of metabolite concentrations, while the Y vector contained information regarding the group (IUGR or NORM). The goodness of fit of the OPLS-DA model (R2Y) was reported, and predictive performance was assessed through cross-validation. Metrics such as the predictive ability of the model (Q2Y) and the predictive ability of permuted models (Q2Y-perm) were calculated for evaluation. OPLS-DA loading plots were used to illustrate the metabolites that contributed the most to the separation between the IUGR and NORM groups. The identification of metabolites of interest was made through the combination of the variable importance in the projection (VIP) and the loading between the metabolite in the X matrix and the predictive latent variable (pLV) of the model. Metabolites with VIP >1.0 and absolute high loading values were considered important in the metabolomics signature (De la Barca et al., 2022).References:Chao de la Barca JM, Chabrun F, Lefebvre T, Roche O, Huetz N, Blanchet O, Legendre G, Simard G, Reynier P, Gascoin G: A Metabolomic Profiling of Intra-Uterine Growth Restriction in Placenta and Cord Blood Points to an Impairment of Lipid and Energetic Metabolism. Biomedicines 2022, 10:1411.

Interpreting the complex interplay of metabolites in heterogeneous biosamples still poses a challenging task. In this study, we propose independent component analysis (ICA) as a multivariate analysis tool for the interpretation of large-scale metabolomics data. In particular, we employ a Bayesian ICA method based on a mean-field approach, which allows us to statistically infer the number of independent components to be reconstructed. The advantage of ICA over correlation-based methods like principal component analysis (PCA) is the utilization of higher order statistical dependencies, which not only yield additional information but also allow a more meaningful representation of the data with fewer components. We performed the described ICA approach on a large-scale metabolomics data set of human serum samples, comprising a total of 1764 study probands with 218 measured metabolites. Inspecting the source matrix of statistically independent metabolite profiles using a weighted enrichment algorithm, we observe strong enrichment of specific metabolic pathways in all components. This includes signatures from amino acid metabolism, energy-related processes, carbohydrate metabolism, and lipid metabolism. Our results imply that the human blood metabolome is composed of a distinct set of overlaying, statistically independent signals. ICA furthermore produces a mixing matrix, describing the strength of each independent component for each of the study probands. Correlating these values with plasma high-density lipoprotein (HDL) levels, we establish a novel association between HDL plasma levels and the branched-chain amino acid pathway. We conclude that the Bayesian ICA methodology has the power and flexibility to replace many of the nowadays common PCA and clustering-based analyses common in the research field.

Horses receiving antimicrobials may develop diarrhea due to changes in the gastrointestinal microbiome and metabolome. This matched, case-controlled study compared the fecal microbiome and metabolome in hospitalized horses on antibiotics that developed diarrhea (AAD), hospitalized horses on antibiotics that did not develop diarrhea (ABX) and a healthy, non-hospitalized control population (CON). Naturally-voided fecal samples were collected from AAD horses (n=17) the day that diarrhea developed and matched to ABX (n=15) and CON (n=31) horses for diet, antimicrobial agent and duration of antimicrobial therapy (< 5 days or > 5 days). Illumina sequencing of 16S rRNA genes on fecal DNA was performed. Alpha and beta diversity metrics were generated using QIIME 2.0. A Kruskal-Wallis with Dunn’s post-test and ANOSIM testing was used for statistical analysis. Microbiome composition in AAD was significantly different from CON (ANOSIM, R= 0.568, p=0.001) and ABX (ANOSIM, R=0.121, p=0.0012). Fecal samples were lyophilized and extracted using a solvent-based method. Untargeted metabolomics using gas chromatography-mass spectrometry platforms was performed. Metabolomic data was analyzed using Metaboanalyst 4.0 and Graphpad Prism v 7. Principal component analysis plots (PCA) were used to visualize the distribution of metabolites between groups. Heat maps were used to identify the relative concentrations amongst the most abundant 25 metabolites. A one-way ANOVA was used to compare differences in metabolites amongst the three groups of horses. Only named metabolites were included in the analysis. The microbiome of AAD and ABX horses had significantly decreased richness and evenness than CON horses (p<0.05). Actinobacteria (q=0.0192) and Bacteroidetes (q=0.0005) were different between AAD and CON. Verrucomicrobia was markedly decreased in AAD compared to ABX and CON horses (q=0.0005). Horses with AAD have a dysbiosis compared to CON horses, and show minor differences in bacterial community composition to ABX horses. Metabolite profiles of horses with AAD clustered separately from those with AAD or CON. Ten metabolites were found to be significantly different between groups (P<0.05) and are listed according to their metabolic pathway: amino acid metabolism (R-equol, L-tyrosine, kynurenic acid, xanthurenic acid, 5-hydroxyindole-3-acetic acid ) lipid metabolism (docosahexaenoic acid ethyl ester), biosynthesis of secondary metabolites (daidzein, isoquinoline) and two metabolites with unidentified pathways (1,3-divinyl-2-imidazolidinone, N-acetyltyramine).

Supporting Dataset for Figure 1a: PCA of Metabolomic Profiles in Sweet Potato under Drought Stress.

This dataset includes the raw metabolomic matrix, sample metadata, R script, and output PCA plot used in Figure 1a of the article:

**"Unveiling Stage-Specific Flavonoid Dynamics Underlying Drought Tolerance in Sweet Potato (*Ipomoea batatas* L.) via Integrative Transcriptomic and Metabolomic Analyses"**

The PCA plot was generated based on metabolite abundance data from sweet potato leaves collected under different drought stress stages (CK, DS1, DS2). The plot visualizes the clustering patterns of samples and highlights treatment-driven variation in metabolite profiles.

### Contents
- `PCA_metabolomics_plot.R`: R script to perform PCA and generate the plot.
- `PCA_metabolomics_data.xlsx`: Input data (Sheet 1: metabolite matrix; Sheet 2: sample group info).
- `PCA_metabolomics_plot.tiff`: Output figure (Figure 1a in the manuscript).
- `README.md`: Detailed instructions and metadata.
- `LICENSE`: CC-BY-4.0 license for reuse and attribution.

This dataset is intended for reproducibility, peer review, and public reuse under an open license.

An untargeted metabolomics approach using UPLC-ESI-QTOF-MS was performed to explore the metabolome of iceberg lettuce and the changes related to storage time and genetics. Two cultivars with different browning susceptibility, fast- browning (FB) and slow-browning (SB) were studied juts after cutting (d0) and after 5 days of storage (d5). Extraction, metabolic profiling, and data-pretreatment procedures were optimized to obtain a robust and reliable data set. Preliminary principal component analysis (PCA) and hierarchical cluster analysis (HCA) of the full dataset (around 8551 extracted, aligned and filtered metabolites) showed a clear separation between the different samples (FB-d0, FB-d5, SB-d0, and SB-d5), highlighting a clear storage time-dependent effect. After statistical analysis applying Student´s t-test, 536 metabolites were detected as significantly different between d0 and d5 of storage in FB and 633 in SB. Some metabolites (221) were common to both cultivars. Out of these significant compounds, 22 were tentatively identified by matching their molecular formulae with those previously reported in the literature. Five families of metabolites were identified, some of them closely related to quality loss: amino acids, phenolic compounds, sesquiterpene lactones, fatty acids, and lysophospholipids. All compounds showed a clear trend to decrease at d5 except phenolic compounds that increased after storage. Overall, cutting and storage were shown to have a significant impact on the changes of lettuce metabolomics, with different trends depending on the browning susceptibility.

The metabolite contents in the jejunum and ileum of Huanjiang mini-pigs were determined using a non-targeted metabolomics approach with the UPLC-HDMS. The metabolomics procedures included sample preparation, metabolite separation and detection, data preprocessing, and statistical analysis. For metabolite identification, approximately 25 mg of each sample was weighed into a 2-mL EP tube and then added 500 mL extract solution (acetonitrile: methanol: water = 2:2:1 (v/v), with the isotopically-labeled internal standard mixture) to the EP tube. After 30 s of vortexing, the mixed samples were homogenized at 35 Hz for 4 min and sonicated in an ice-water bath for 5 min. The homogenization and sonication cycles were repeated three times. Then the samples were incubated for 1 h at -40 °C and centrifuged at 12,000 ´ g for 15 min at 4 °C. The resulting supernatants were filtered through a 0.22-µm membrane and transferred to fresh glass vials for further analysis. The quality control (QC) sample was obtained by mixing an equal aliquot of the supernatants from all samples. An ultra-performance liquid chromatography (UPLC) system (Vanquish, Thermo Fisher Scientific, Waltham, MA, USA) with a UPLC BEH Amide column (2.10 × 100 mm, 1.70 mm) coupled with Q Exactive HFX mass spectrometer (Orbitrap MS, Thermo Fisher Scientific, Waltham, MA, USA) was used to perform LC-MS/MS analyses. The mobile phase A contained 25 mmol/L ammonium acetate and 25 mmol/L ammonia hydroxide in water, and the mobile phase B contained acetonitrile. The injection volume was 3 mL, and the temperature of the auto-sampler was set at 4 °C. To acquire MS/MS spectra on an information-dependent acquisition (IDA) mode, the QE HFX mass spectrometer was used for its ability in the control of the acquisition software (Xcalibur, Thermo Fisher Scientific, Waltham, MA, USA). In this mode, the acquisition software continuously evaluated the full scan of the MS spectrum. The conditions for ESI source were set as follows: sheath gas flow rate 30 Arb, Aux gas flow rate 25 Arb, capillary temperature 350 °C, full MS resolution 60,000, MS/MS resolution a7500, collision energy 10/30/60 in NCE mode, and spray voltage 3.60 kV (positive ion mode) or -3.20 kV (negative ion mode), respectively. For peak detection, extraction, alignment, and integration, obtained raw data were converted into mzXML format by ProteoWizard and then processed with an in-house program, which was developed using R and based on XCMS. The metabolites were annotated using an in-house MS2 (secondary mass spectrometry) database (BiotreeDB v2.1). The value of the cutoff was 0.3. The PCA and orthogonal partial least squares discriminant analysis (OPLS-DA) were established by the SIMCA software v.16.0.2 (Sartorious Stedim Data Analytics AB, Umea, Sweden) to visualize the distinction and detect differential metabolites among different CP content groups. Moreover, the Kyoto Encyclopedia of Genes and Genomes (KEGG) and MetaboAnalyst 5.0 were used for pathway analysis.

OBJECTIVE: Childhood obesity is a strong risk factor for adult obesity and metabolic diseases, including type 2 diabetes and cardiovascular disease. Early lifestyle intervention in children with obesity reduces future disease risk. The objective of this study is to identify metabolic signatures associated with lifestyle intervention in prepubertal children with obesity.METHODS: Thirty-five prepubertal children (7-10 years) with obesity (BMI>2 standard deviations) were enrolled in the study and participated in a 6-month-long lifestyle intervention program. Physiological and biochemical data and blood samples were collected both at baseline and after the intervention. A liquid chromatography-mass spectrometry (LC-MS)-based metabolomics approach was applied to obtain a comprehensive profiling of plasma samples, identifying 2581 distinct metabolite. Principal component analysis (PCA) was performed to consolidate all features into 8 principal components. Associations between metabolites and physiological and biochemical variables were investigated.RESULTS: The intervention program significantly decreased mean (95% CI) BMI standard deviation score from 3.56 (3.29-3.84) to 3.11 (2.88-3.34) (P<0.001). PCA identified one component (PC1) significantly altered by the intervention (Bonferroni adjusted P=0.008). A sphingolipid metabolism-related signature was identified as the major contributor to PC1. Sphingolipid metabolites were decreased by the intervention, and included multiple sphingomyelin, ceramide, glycosylsphingosine, and sulfatide species. Changes in several sphingolipid metabolites were associated with intervention-induced improvements in HbA1c levels.CONCLUSIONS: Decreased circulating sphingolipid-related metabolites were associated with lifestyle intervention in prepubertal children with obesity, and correlated to improvements in HbA1c.

Factor loading tables produced by two PCA run on only daytime samples or only nighttime samples, using the interactive dataset

Non-targeted metabolomics study of serum samples from prostate cancer and healthy individuals using flow injection ion mobility mass spectrometry

Few data are available that describe how probiotics influence systemic metabolism during endurance exercise. Metabolomic profiling of endurance athletes will elucidate mechanisms by which probiotics may confer benefits to the athlete. In this study, twenty-four runners (20 male, 4 female) were block randomised into two groups using a double-blind matched-pairs design according to their most recent Marathon performance. Runners were assigned to 28-days of supplementation with a multi-strain probiotic (PRO) or a placebo (PLB). Following 28-days of supplementation, runners performed a competitive track Marathon race. Venous blood samples and muscle biopsies (vastus lateralis) were collected on the morning of the race and immediately post-race. Samples were subsequently analysed by untargeted 1H-NMR metabolomics. Principal component analysis (PCA) identified a greater difference in the post-Marathon serum metabolome in the PLB group vs. PRO. Univariate tests identified 17 non-overlapped metabolites in PLB, whereas only seven were identified in PRO. By building a PLS-DA model of two components, we revealed combinations of metabolites able to discriminate between PLB and PRO post-Marathon. PCA of muscle biopsies demonstrated no discernible difference post-Marathon between treatment groups. In conclusion, 28-days of probiotic supplementation alters the metabolic perturbations induced by a Marathon. Such findings may be related to maintaining the integrity of the gut during endurance exercise.

The differences in pungency of 'sirih' imply the probable occurrence of several variants of Piper betle L. in Malaysia. However, the metabolite profiles underlying the pungency of the different variants remain a subject of further research. The differences in metabolite profiles of selected Malaysian P. betle variants were thus investigated; specifically, the leaf aqueous methanolic extracts and essential oils were analyzed via 1H-NMR and GC-MS metabolomics, respectively. Principal component analysis (PCA) of the 1H-NMR spectral data showed quantitative differences in the metabolite profiles of 'sirih melayu' and 'sirih india' and revealed an ambiguous group of samples with low acetic acid content, which was identified as Piper rubro-venosum hort. ex Rodigas based on DNA sequences of the internal transcribed spacer 2 (ITS2) region. The finding was supported by PCA of two GC-MS datasets of P. betle samples obtained from several states in Peninsular Malaysia, which displayed clustering of the samples into 'sirih melayu' and 'sirih india' groups. Higher abundance of chavicol acetate was consistently found to be characteristic of 'sirih melayu'. The present research has provided preliminary evidence supporting the notion of occurrence of two P. betle variants in Malaysia based on chemical profiles, which may be related to the different genders of P. betle.

Untargeted mass spectrometry metabolomics is an increasingly popular approach for characterizing complex mixtures. Recent studies have highlighted the impact of data pre-processing for determining the quality of metabolomics data analysis. The first step in data processing with untargeted metabolomics requires that signal thresholds be selected for which features (detected ions) are included in the dataset. Analysts face the challenge of knowing where to set these thresholds; setting them too high could mean missing relevant features but setting them too low could result in a complex and unwieldy dataset. This study compared data interpretation for an example metabolomics dataset when intensity thresholds were set at a range of feature heights. The main observations were that low signal thresh-olds 1) improved limit of detection, 2) increased the number of features detected with an associated isotope pattern and/or MS-MS fragmentation spectrum and 3) increased the number of in-source clusters and fragments detected for known analytes of interest. When the settings of parameters differing in intensities were applied on a set of 39 samples to discriminate the samples through principal component analyses (PCA), similar results were obtained with both low and high-intensity thresholds. We conclude that the most information-rich datasets can be obtained by setting low-intensity thresholds. However, in cases where only a qualitative comparison of samples with PCA is to be performed, it may be sufficient to set high thresholds and thereby reduce the complexity of the data processing and amount of computational time required.

During our search for active substances capable of inhibiting a newly discovered highly lethal Vibrio strain vp-HL causing highly lethal Vibrio disease in shrimp (HLVD), we found that the fermentation broth of multiple Bacillus strains exhibited antibacterial activity. However, the substances responsible for the activity remained unclear. Metabolomics was employed in conjunction with bioactivity screening to identify the antibacterial compounds from Bacillus strains. The Ethyl Acetate extracts of the broth of 20 Bacillus strains were analyzed using UPLC-MS/MS. Principal Component Analysis (PCA) and Orthogonal Partial Least Square - Discriminant Analysis (OPLS-DA) was used for pattern recognition. The strains fall into two groups in the scores plot from PCA. The S-plot from OPLS-DA offered information on biomarkers.

Polycystic kidney disease (PKD) encompasses a spectrum of inherited disorders that lead to end-stage renal disease (ESRD). There is no cure for PKD and current treatment options are limited to renal replacement therapy and transplantation. A better understanding of the pathobiology of PKD is needed for the development of new, less invasive treatments. The Lewis Polycystic Kidney (LPK) rat phenotype has been characterized and classified as a model of nephronophthisis (NPHP9, caused by mutation of the Nek8 gene) for which polycystic kidneys are one of the main pathologic features. The aim of this study was to use a GC-MS-based untargeted metabolomics approach to determine key biochemical changes in kidney and liver tissue of the LPK rat. Tissues from 16-week old LPK (n = 10) and Lewis age- and sex-matched control animals (n = 11) were used. Principal component analysis (PCA) distinguished signal corrected metabolite profiles from Lewis and LPK rats for kidney (PC-1 77%) and liver (PC-1 46%) tissue. There were marked differences in the metabolite profiles of the kidney tissues with 122 deconvoluted features significantly different between the LPK and Lewis strains. The metabolite profiles were less marked between strains for liver samples with 30 features significantly different. Five biochemical pathways showed three or more significantly altered metabolites: transcription/translation, arginine and proline metabolism, alpha-linolenic and linoleic acid metabolism, the citric acid cycle, and the urea cycle. The results of this study validate and complement the current literature and are consistent with the understood pathobiology of PKD.

Metabolomic analyses raw files, Compounds analyses, Hierarchical Condition tress and PCA Scores are uploaded.

Information on crop genotype- and phenotype-metabolite associations can be of value to trait development as well as to food security and safety. The unique study presented here assessed seed metabolomic and ionomic diversity in a soybean (Glycine max) lineage representing ~35 years of breeding (launch years 1972–2008) and increasing yield potential. Selected varieties included six conventional and three genetically modified (GM) glyphosate-tolerant lines. A metabolomics approach utilizing capillary electrophoresis (CE)-time-of-flight-mass spectrometry (TOF-MS), gas chromatography (GC)-TOF-MS and liquid chromatography (LC)-quadrupole (q)-TOFMS resulted in measurement of a total of 732 annotated peaks. Ionomics through inductively-coupled plasma (ICP)-MS profiled twenty mineral elements. Orthogonal partial least squares-discriminant analysis (OPLS-DA) of the seed data successfully differentiated newer higher-yielding soybean from earlier lower-yielding accessions at both field sites. This result reflected genetic fingerprinting data that demonstrated a similar distinction between the newer and older soybean. Correlation analysis also revealed associations between yield data and specific metabolites. There were no clear metabolic differences between the conventional and GM lines. Overall, observations of metabolic and genetic differences between older and newer soybean varieties provided novel and significant information on the impact of varietal development on biochemical variability. Proposed applications of omics in food and feed safety assessments will need to consider that GM is not a major source of metabolite variability and that trait development in crops will, of necessity, be associated with biochemical variation. Resources in this dataset:Resource Title: Pointer to Electronic Supplementary Material. File Name: Web Page, url: https://link.springer.com/article/10.1007/s11306-014-0702-6#Sec17 Link to Electronic Supplementary Material at Metabolomics. Files are: Supplementary material 1: Full metabolite profile and ionomic dataset - Download Excel Supplementary material 2: List of annotated metabolites - Download Excel List of the 681 annotated metabolites obtained by using 4 platforms after data summarization and removal of 50% missing value in the metabolite profile data. Some metabolite peaks could not be summarized and thus kept in the list. Supplementary material 3: Monsanto_ionomics_Data_Baxterla - Download Excel Supplementary material 4: Metabolomics metadata - Download .docx Plant context metadata; Chemical analysis metadata. Supplementary material 5: Spearman correlations between yield and metabolites/ions - Download Excel Supplementary material 6: Supporting Tables and Figures - Download .docx Supporting Table 1.Similarity matrix: Genetic similarity of different soybean varieties based on genetic fingerprint data. Supporting Table 2. Metabolite Coverage of Analytical Platforms. Supporting Table 3. Summary of Statistically Significant Differences in Ionomic Profiles. Supporting Figure A. PCA (principal components one and two) based on the genotypic data of 1,484 pre-commercial and commercial proprietary Monsanto lines. Supporting Fig. 1. Evaluation of the achieved coverage of metabolite profile data. Supporting Fig. 2. Principal component analysis of the identified or annotated metabolites/ peaks. Supporting Fig. 3. Principal component analysis of the identified or annotated metabolites/peaks and including the ionomics data. Supporting Fig. 4. The score scatter plot of OPLS-DA using the identified or annotated metabolites/ peaks and including the ionomics data. Supporting Fig. 5. Graphic representation of nodes of the first neighbors in the yield-to-metabolite correlation networks of samples harvested at ILJA and ILJE.

Synthetic cathinones belong to the most often seized new psychoactive substances on an international level. This study investigated the toxicometabolomics, particularly the in vitro metabolism of 2-(methylamino)-1-(4-methylphenyl)-1-pentanone (4-MPD) and 2-(ethylamino)-1-(4-methylphenyl)-1-pentanone (4-MEAP) in pooled human liver microsomes (pHLM) using untargeted metabolomics techniques. Incubations were performed with the substrates in concentrations ranging from 0, 12.5, and 25 µM. Analysis was done by means of high-performance liquid chromatography coupled to high-resolution mass spectrometry (HPLC-HRMS/MS) in full scan only and the obtained data was evaluated using XCMS Online and MetaboAnalyst. Significant features were putatively identified using a separate parallel reaction monitoring method. Statistical analysis was performed using Kruskal-Wallis test for prefiltering significant features and subsequent hierarchical clustering, as well as principal component analysis (PCA). Hierarchical clustering or PCA showed a distinct clustering of all concentrations with most of the features z-scores rising with the concentration of the investigated substances. Identification of significant features left many of them unidentified but revealed metabolites of both 4-MPD and 4-MEAP. Both substances formed carboxylic acids, were hydroxylated at the alkyl chain, and formed metabolites after combined hydroxylation and reduction of the cathinone oxo group. 4-MPD additionally formed a dihydroxy metabolite and a hydroxylamine. 4-MEAP was additionally found reduced at the cathinone oxo group, N-dealkylated, and formed an oxo metabolite. These findings are the first to describe the metabolic pathways of 4-MPD and to extend our knowledge about the metabolism of 4-MEAP. Findings, particularly the MS data of the metabolites, are essential for setting up metabolite-based toxicological (urine) screening procedures.

Better understanding of the molecular changes associated with disease is essential for identifying new routes to improved therapeutics and diagnostic tests. The aim of this study was to investigate the dynamic changes in the metabolic profile of mouse sera during T. gondii infection. We carried out untargeted metabolomic analysis of sera collected from female BALB/c mice experimentally infected with the T. gondii Pru strain (Genotype II). Serum samples were collected at 7, 14 and 21 day post infection (DPI) from infected and control mice and were subjected to liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS)-based global metabolomics analysis. Multivariate statistical analysis identified 79 differentially expressed metabolites in ESI+ mode and 74 in ESI− mode in sera of T. gondii-infected mice compared to the control mice. Further principal component analysis (PCA) and partial least squares-discrimination analysis (PLS-DA) identified 19 dysregulated metabolites (5 in ESI+ mode and 14 in ESI− mode) related to the metabolism of amino acids and energy metabolism. The potential utility of these metabolites as diagnostic biomarkers was validated through receiver operating characteristic (ROC) curve analysis. These findings provide putative metabolite biomarkers for future study and allow for hypothesis generation about the pathophysiology of toxoplasmosis.

LC-MS plant non target metabolomics data consists of 12 samples, including Venn analysis, cluster analysis, metabolite correlation analysis, sample correlation heatmap, PCA analysis, Wien analysis, PLS-DA analysis, phytochemical classification, KEGG chemical classification, KEGG functional pathways, HMDB compound classification, metabolite annotation, metabolite overview, and differential metabolite analysis.

One of the first steps in analyzing high-dimensional functional genomics data is an exploratory analysis of such data. Cluster Analysis and Principal Component Analysis are then usually the method of choice. Despite their versatility they also have a severe drawback: they do not always generate simple and interpretable solutions. On the basis of the observation that functional genomics data often contain both informative and non-informative variation, we propose a method that finds sets of variables containing informative variation. This informative variation is subsequently expressed in easily interpretable simplivariate components.We present a new implementation of the recently introduced simplivariate models. In this implementation, the informative variation is described by multiplicative models that can adequately represent the relations between functional genomics data. Both a simulated and two real-life metabolomics data sets show good performance of the method.