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In five consecutive years lettuce, spinach, spring wheat, endive and maize were grown in pots and the effects of native and soil-applied Zn and Cd on plant Zn and Cd concentrations were studied. The normal interactive pattern was antagonistic, Zn reducing plant Cd uptake, and conversely, but less so. Only in loam soil Zn and Cd were synergistic to some extent, plant Zn uptake increasing with applied Cd.When relating total soil Cd/Zn to plant Cd/Zn separate sets of data could be distinguished for loam and sandy soil, each fitting a straight line. The use of 0.1 M CaCl2 instead of total extractable soil Cd/Zn makes the two sets of data to coalesce around a single straight line. All crops were found to show a positive linear relationship between 0.1 M CaCl2-extractable soil Cd/Zn and plant Cd/Zn. 相似文献
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Applications of a new subspace clustering algorithm (COSA) in medical systems biology 总被引:1,自引:0,他引:1
Doris Damian Matej Orešič Elwin Verheij Jacqueline Meulman Jerome Friedman Aram Adourian Nicole Morel Age Smilde Jan van der Greef 《Metabolomics : Official journal of the Metabolomic Society》2007,3(1):69-77
A novel clustering approach named Clustering Objects on Subsets of Attributes (COSA) has been proposed (Friedman and Meulman,
(2004). Clustering objects on subsets of attributes. J. R. Statist. Soc. B 66, 1–25.) for unsupervised analysis of complex data sets. We demonstrate its usefulness in medical systems biology studies.
Examples of metabolomics analyses are described as well as the unsupervised clustering based on the study of disease pathology
and intervention effects in rats and humans. In comparison to principal components analysis and hierarchical clustering based
on Euclidean distance, COSA shows an enhanced capability to trace partial similarities in groups of objects enabling a new
discovery approach in systems biology as well as offering a unique approach to reveal common denominators of complex multi-factorial
diseases in animal and human studies.
Doris Damian, Matej Orešič, and Elwin Verheij contributed equally to this work. 相似文献
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DM Hendrickx HC Hoefsloot MM Hendriks DJ Vis AB Canelas B Teusink AK Smilde 《Molecular bioSystems》2012,8(9):2415-2423
Elucidating changes in the distribution of reaction rates in metabolic pathways under different conditions is a central challenge in systems biology. Here we present a method for inferring regulation mechanisms responsible for changes in the distribution of reaction rates across conditions from correlations in time-resolved data. A reversal of correlations between conditions reveals information about regulation mechanisms. With the use of a small in silico hypothetical network, based on only the topology and directionality of a known pathway, several regulation scenarios can be formulated. Confronting these scenarios with experimental data results in a short list of possible pathway regulation mechanisms associated with the reversal of correlations between conditions. This procedure allows for the formulation of regulation scenarios without detailed prior knowledge of kinetics and for the inference of reaction rate changes without rate information. The method was applied to experimental time-resolved metabolomics data from multiple short-term perturbation-response experiments in S. cerevisiae across aerobic and anaerobic conditions. The method's output was validated against a detailed kinetic model of glycolysis in S. cerevisiae, which showed that the method can indeed infer the correct regulation scenario. 相似文献
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Smit S Hoefsloot HC Smilde AK 《Journal of chromatography. B, Analytical technologies in the biomedical and life sciences》2008,866(1-2):77-88
This review discusses data analysis strategies for the discovery of biomarkers in clinical proteomics. Proteomics studies produce large amounts of data, characterized by few samples of which many variables are measured. A wealth of classification methods exists for extracting information from the data. Feature selection plays an important role in reducing the dimensionality of the data prior to classification and in discovering biomarker leads. The question which classification strategy works best is yet unanswered. Validation is a crucial step for biomarker leads towards clinical use. Here we only discuss statistical validation, recognizing that biological and clinical validation is of utmost importance. First, there is the need for validated model selection to develop a generalized classifier that predicts new samples correctly. A cross-validation loop that is wrapped around the model development procedure assesses the performance using unseen data. The significance of the model should be tested; we use permutations of the data for comparison with uninformative data. This procedure also tests the correctness of the performance validation. Preferably, a new set of samples is measured to test the classifier and rule out results specific for a machine, analyst, laboratory or the first set of samples. This is not yet standard practice. We present a modular framework that combines feature selection, classification, biomarker discovery and statistical validation; these data analysis aspects are all discussed in this review. The feature selection, classification and biomarker discovery modules can be incorporated or omitted to the preference of the researcher. The validation modules, however, should not be optional. In each module, the researcher can select from a wide range of methods, since there is not one unique way that leads to the correct model and proper validation. We discuss many possibilities for feature selection, classification and biomarker discovery. For validation we advice a combination of cross-validation and permutation testing, a validation strategy supported in the literature. 相似文献
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Edoardo Saccenti Age K. Smilde José Camacho 《Metabolomics : Official journal of the Metabolomic Society》2018,14(6):73
Introduction
Modern omics experiments pertain not only to the measurement of many variables but also follow complex experimental designs where many factors are manipulated at the same time. This data can be conveniently analyzed using multivariate tools like ANOVA-simultaneous component analysis (ASCA) which allows interpretation of the variation induced by the different factors in a principal component analysis fashion. However, while in general only a subset of the measured variables may be related to the problem studied, all variables contribute to the final model and this may hamper interpretation.Objectives
We introduce here a sparse implementation of ASCA termed group-wise ANOVA-simultaneous component analysis (GASCA) with the aim of obtaining models that are easier to interpret.Methods
GASCA is based on the concept of group-wise sparsity introduced in group-wise principal components analysis where structure to impose sparsity is defined in terms of groups of correlated variables found in the correlation matrices calculated from the effect matrices.Results
The GASCA model, containing only selected subsets of the original variables, is easier to interpret and describes relevant biological processes.Conclusions
GASCA is applicable to any kind of omics data obtained through designed experiments such as, but not limited to, metabolomic, proteomic and gene expression data.10.
Tunahan Çakır Margriet M. W. B. Hendriks Johan A. Westerhuis Age K. Smilde 《Metabolomics : Official journal of the Metabolomic Society》2009,5(3):318-329
Reverse engineering of high-throughput omics data to infer underlying biological networks is one of the challenges in systems
biology. However, applications in the field of metabolomics are rather limited. We have focused on a systematic analysis of
metabolic network inference from in silico metabolome data based on statistical similarity measures. Three different data
types based on biological/environmental variability around steady state were analyzed to compare the relative information
content of the data types for inferring the network. Comparing the inference power of different similarity scores indicated
the clear superiority of conditioning or pruning based scores as they have the ability to eliminate indirect interactions.
We also show that a mathematical measure based on the Fisher information matrix gives clues on the information quality of
different data types to better represent the underlying metabolic network topology. Results on several datasets of increasing
complexity consistently show that metabolic variations observed at steady state, the simplest experimental analysis, are already
informative to reveal the connectivity of the underlying metabolic network with a low false-positive rate when proper similarity-score
approaches are employed. For experimental situations this implies that a single organism under slightly varying conditions
may already generate more than enough information to rightly infer networks. Detailed examination of the strengths of interactions
of the underlying metabolic networks demonstrates that the edges that cannot be captured by similarity scores mainly belong
to metabolites connected with weak interaction strength. 相似文献