Binary Simplification as an Effective Tool in Metabolomics Data Analysis.
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ABSTRACT:
SUBMITTER: Traquete F
PROVIDER: S-EPMC8621519 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:MotivationMetabolomics is a rapidly evolving field that holds promise to provide insights into genotype-phenotype relationships in cancers, diabetes and other complex diseases. One of the major informatics challenges is providing tools that link metabolite data with other types of high-throughput molecular data (e.g. transcriptomics, proteomics), and incorporate prior knowledge of pathways and molecular interactions.ResultsWe describe a new, substantially redesigned version of our tool Metscape that allows users to enter experimental data for metabolites, genes and pathways and display them in the context of relevant metabolic networks. Metscape 2 uses an internal relational database that integrates data from KEGG and EHMN databases. The new version of the tool allows users to identify enriched pathways from expression profiling data, build and analyze the networks of genes and metabolites, and visualize changes in the gene/metabolite data. We demonstrate the applications of Metscape to annotate molecular pathways for human and mouse metabolites implicated in the pathogenesis of sepsis-induced acute lung injury, for the analysis of gene expression and metabolite data from pancreatic ductal adenocarcinoma, and for identification of the candidate metabolites involved in cancer and inflammation.AvailabilityMetscape is part of the National Institutes of Health-supported National Center for Integrative Biomedical Informatics (NCIBI) suite of tools, freely available at http://metscape.ncibi.org. It can be downloaded from http://cytoscape.org or installed via Cytoscape plugin manager.Contactmetscape-help@umich.edu; akarnovs@umich.eduSupplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC3268237 | biostudies-literature
Project description:BackgroundMetabolomics datasets are often high-dimensional though only a limited number of variables are expected to be informative given a specific research question. The important task of selecting informative variables can therefore become complex. In this paper we look at discriminating between two groups. Two tasks need to be performed: (i) finding variables which differ between the two groups; and (ii) determining how the selected variables can be used to classify new subjects. We introduce an approach using minimum classification error rates as test statistics to find discriminatory and therefore informative variables. The thresholds resulting in the minimum error rates can be used to classify new subjects. This approach transforms error rates into p-values and is referred to as ERp.ResultsWe show that non-parametric hypothesis testing, based on minimum classification error rates as test statistics, can find statistically significantly shifted variables. The discriminatory ability of variables becomes more apparent when error rates are evaluated based on their corresponding p-values, as relatively high error rates can still be statistically significant. ERp can handle unequal and small group sizes, as well as account for the cost of misclassification. ERp retains (if known) or reveals (if unknown) the shift direction, aiding in biological interpretation. The threshold resulting in the minimum error rate can immediately be used to classify new subjects. We use NMR generated metabolomics data to illustrate how ERp is able to discriminate subjects diagnosed with Mycobacterium tuberculosis infected meningitis from a control group. The list of discriminatory variables produced by ERp contains all biologically relevant variables with appropriate shift directions discussed in the original paper from which this data is taken.ConclusionsERp performs variable selection and classification, is non-parametric and aids biological interpretation while handling unequal group sizes and misclassification costs. All this is achieved by a single approach which is easy to perform and interpret. ERp has the potential to address many other characteristics of metabolomics data. Future research aims to extend ERp to account for a large proportion of observations below the detection limit, as well as expand on interactions between variables.
| S-EPMC4712617 | biostudies-literature
Project description:Compound identification is a critical step in untargeted metabolomics. Its most important procedure is to calculate the similarity between experimental mass spectra and either predicted mass spectra or mass spectra in a mass spectral library. Unlike the continuous similarity measures, there is no study to assess the performance of binary similarity measures in compound identification, even though the well-known Jaccard similarity measure has been widely used without proper evaluation. The objective of this study is thus to evaluate the performance of binary similarity measures for compound identification in untargeted metabolomics. Fifteen binary similarity measures, including the well-known Jaccard, Dice, Sokal-Sneath, Cosine, and Simpson measures, were selected to assess their performance in compound identification. using both electron ionization (EI) and electrospray ionization (ESI) mass spectra. Our theoretical evaluations show that the accuracy of the compound identification was exactly the same between the Jaccard, Dice, 3W-Jaccard, Sokal-Sneath, and Kulczynski measures, between the Cosine and Hellinger measures, and between the McConnaughey and Driver-Kroeber measures, which were practically confirmed using mass spectra libraries. From the mass spectrum-based evaluation, we observed that the best performing similarity measures were the McConnaughey and Driver-Kroeber measures for EI mass spectra and the Cosine and Hellinger measures for ESI mass spectra. The most robust similarity measure was the Fager-McGowan measure, the second-best performing similarity measure in both EI and ESI mass spectra.
| S-EPMC9394311 | biostudies-literature
Project description:We develop a new principal components analysis (PCA) type dimension reduction method for binary data. Different from the standard PCA which is defined on the observed data, the proposed PCA is defined on the logit transform of the success probabilities of the binary observations. Sparsity is introduced to the principal component (PC) loading vectors for enhanced interpretability and more stable extraction of the principal components. Our sparse PCA is formulated as solving an optimization problem with a criterion function motivated from penalized Bernoulli likelihood. A Majorization-Minimization algorithm is developed to efficiently solve the optimization problem. The effectiveness of the proposed sparse logistic PCA method is illustrated by application to a single nucleotide polymorphism data set and a simulation study.
| S-EPMC2992445 | biostudies-literature
Project description:The Polyomics integrated Metabolomics Pipeline (PiMP) fulfils an unmet need in metabolomics data analysis. PiMP offers automated and user-friendly analysis from mass spectrometry data acquisition to biological interpretation. Our key innovations are the Summary Page, which provides a simple overview of the experiment in the format of a scientific paper, containing the key findings of the experiment along with associated metadata; and the Metabolite Page, which provides a list of each metabolite accompanied by 'evidence cards', which provide a variety of criteria behind metabolite annotation including peak shapes, intensities in different sample groups and database information.PiMP is available at http://polyomics.mvls.gla.ac.uk, and access is freely available on request. 50?GB of space is allocated for data storage, with unrestricted number of samples and analyses per user. Source code is available at https://github.com/RonanDaly/pimp and licensed under the GPL.karl.burgess@glasgow.ac.uk.Supplementary data are available at Bioinformatics online.
| S-EPMC5860087 | biostudies-other
Project description:A sequence analysis-oriented binary search-like algorithm was transformed to a sensitive and accurate analysis tool for processing whole-genome data. The advantage of the algorithm over previous methods is its ability to detect the margins of both short and long genome fragments, enriched by up-regulated signals, at equal accuracy. The score of an enriched genome fragment reflects the difference between the actual concentration of up-regulated signals in the fragment and the chromosome signal baseline. The "divide-and-conquer"-type algorithm detects a series of nonintersecting fragments of various lengths with locally optimal scores. The procedure is applied to detected fragments in a nested manner by recalculating the lower-than-baseline signals in the chromosome. The algorithm was applied to simulated whole-genome data, and its sensitivity/specificity were compared with those of several alternative algorithms. The algorithm was also tested with four biological tiling array datasets comprising Arabidopsis (i) expression and (ii) histone 3 lysine 27 trimethylation CHIP-on-chip datasets; Saccharomyces cerevisiae (iii) spliced intron data and (iv) chromatin remodeling factor binding sites. The analyses' results demonstrate the power of the algorithm in identifying both the short up-regulated fragments (such as exons and transcription factor binding sites) and the long--even moderately up-regulated zones--at their precise genome margins. The algorithm generates an accurate whole-genome landscape that could be used for cross-comparison of signals across the same genome in evolutionary and general genomic studies.
| S-EPMC2947915 | biostudies-literature
Project description:Metabolomics aims at a complete characterization of all metabolites in biological samples in terms of both their identities and concentrations. Because changes of metabolites and their concentrations are a direct reflection of cellular activity, it allows for a better understanding of cellular processes and function to be obtained. Although NMR spectroscopy is routinely applied to complex biological mixtures without purification, overlapping NMR peaks often pose a challenge for the comprehensive and accurate identification of the underlying metabolites. To address this problem, we present a novel nanoparticle-based strategy that differentiates between metabolites based on their electric charge. By adding electrically charged silica nanoparticles to the solution NMR sample, metabolites of opposite charge bind to the nanoparticles and their NMR signals are weakened or entirely suppressed due to peak broadening caused by the slow rotational tumbling of the nanometer-sized nanoparticles. Comparison of the edited with the original spectrum significantly facilitates analysis and reduces ambiguities in the identification of metabolites. This method makes NMR directly sensitive to the detection of molecular charges at constant pH, as demonstrated here both for model mixtures and human urine. The simplicity of the approach should make it useful for a wide range of metabolomics applications.
| S-EPMC5358793 | biostudies-literature
Project description:Methods to modify gene function in mice with high spatiotemporal resolution have revolutionized biomedical research, with Cre-loxP technology remaining the most widely used. We previously generated the iSuRe-Cre mouse allele, a dual Cre-reporter line that, by permanently expressing Cre in reporter-expressing cells, provides certainty that these cells recombine other floxed alleles. Here we report the generation, validation, and utility of a second-generation allele, iSuRe-HadCre. In contrast to its predecessor, the new allele is targeted to the ubiquitously expressed Rosa26 locus, shows no leakiness, is significantly brighter and more sensitive to CreERT2 activity, and has no Cre-related toxicity. iSuRe-HadCre achieves this through a novel inbuilt recombinase genetic switch that ensures that cells expressing a fluorescent reporter have transient, but sufficient, expression of Cre. This new tool will be essential for the precise, efficient, and trustworthy analysis of gene function in single cells or entire tissues of mice.
2024-07-25 | GSE245726 | GEO
Project description:BackgroundMetabolomics has the promise to transform the area of personalized medicine with the rapid development of high throughput technology for untargeted analysis of metabolites. Open access, easy to use, analytic tools that are broadly accessible to the biological community need to be developed. While technology used in metabolomics varies, most metabolomics studies have a set of features identified. Galaxy is an open access platform that enables scientists at all levels to interact with big data. Galaxy promotes reproducibility by saving histories and enabling the sharing workflows among scientists.ResultsSECIMTools (SouthEast Center for Integrated Metabolomics) is a set of Python applications that are available both as standalone tools and wrapped for use in Galaxy. The suite includes a comprehensive set of quality control metrics (retention time window evaluation and various peak evaluation tools), visualization techniques (hierarchical cluster heatmap, principal component analysis, modular modularity clustering), basic statistical analysis methods (partial least squares - discriminant analysis, analysis of variance, t-test, Kruskal-Wallis non-parametric test), advanced classification methods (random forest, support vector machines), and advanced variable selection tools (least absolute shrinkage and selection operator LASSO and Elastic Net).ConclusionsSECIMTools leverages the Galaxy platform and enables integrated workflows for metabolomics data analysis made from building blocks designed for easy use and interpretability. Standard data formats and a set of utilities allow arbitrary linkages between tools to encourage novel workflow designs. The Galaxy framework enables future data integration for metabolomics studies with other omics data.
| S-EPMC5910624 | biostudies-literature
Project description:BackgroundAnalysis of dynamic metabolomics data holds the promise to improve our understanding of underlying mechanisms in metabolism. For example, it may detect changes in metabolism due to the onset of a disease. Dynamic or time-resolved metabolomics data can be arranged as a three-way array with entries organized according to a subjects mode, a metabolites mode and a time mode. While such time-evolving multiway data sets are increasingly collected, revealing the underlying mechanisms and their dynamics from such data remains challenging. For such data, one of the complexities is the presence of a superposition of several sources of variation: induced variation (due to experimental conditions or inborn errors), individual variation, and measurement error. Multiway data analysis (also known as tensor factorizations) has been successfully used in data mining to find the underlying patterns in multiway data. To explore the performance of multiway data analysis methods in terms of revealing the underlying mechanisms in dynamic metabolomics data, simulated data with known ground truth can be studied.ResultsWe focus on simulated data arising from different dynamic models of increasing complexity, i.e., a simple linear system, a yeast glycolysis model, and a human cholesterol model. We generate data with induced variation as well as individual variation. Systematic experiments are performed to demonstrate the advantages and limitations of multiway data analysis in analyzing such dynamic metabolomics data and their capacity to disentangle the different sources of variations. We choose to use simulations since we want to understand the capability of multiway data analysis methods which is facilitated by knowing the ground truth.ConclusionOur numerical experiments demonstrate that despite the increasing complexity of the studied dynamic metabolic models, tensor factorization methods CANDECOMP/PARAFAC(CP) and Parallel Profiles with Linear Dependences (Paralind) can disentangle the sources of variations and thereby reveal the underlying mechanisms and their dynamics.
| S-EPMC8750750 | biostudies-literature