Adaptive binning: An improved binning method for metabolomics data using the undecimated wavelet transform

Davis, Richard A.; Charlton, Adrian J.; Godward, John; Jones, Stephen A.; Harrison, Mark D.; Wilson, Julie

doi:10.1016/j.chemolab.2006.08.014

Cited by 94 publications

(60 citation statements)

References 18 publications

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“…Analysis and comparison of metabolite profiles from different groups of plants utilised adaptive binning of the 1D 1 H NMR datasets (Davis et al 2007). Up to 656 data regions were identified within leaf NMR spectra that related to NMR resonance peaks.…”

Section: Global Analysis Of Nmr Metabolite Profiles Of Leaves From Drmentioning

confidence: 99%

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Responses of the pea (Pisum sativum L.) leaf metabolome to drought stress assessed by nuclear magnetic resonance spectroscopy

Charlton¹,

Donarski²,

Harrison³

et al. 2008

Metabolomics

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116

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While many compounds have been reported to change in laboratory based drought-stress experiments, little is known about how such compounds change, and are significant, under field conditions. The Pisum sativum L. (pea) leaf metabolome has been profiled, using 1D and 2D NMR spectroscopy, to monitor the changes induced by drought-stress, under both glasshouse and simulated field conditions. Significant changes in resonances were attributed to a range of compounds, identified as both primary and secondary metabolites, highlighting metabolic pathways that are stress-responsive. Importantly, these effects were largely consistent among different experiments with highly diverse conditions. The metabolites that were present at significantly higher concentrations in droughtstressed plants under all growth conditions included proline, valine, threonine, homoserine, myoinositol, c-aminobutyrate (GABA) and trigonelline (nicotinic acid betaine). Metabolites that were altered in relative amounts in different experiments, but not specifically associated with drought-stress, were also identified. These included glutamate, asparagine and malate, with the last being present at up to 5-fold higher concentrations in plants grown in field experiments. Such changes may be expected to impact both on plant performance and crop end-use.

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Section: Global Analysis Of Nmr Metabolite Profiles Of Leaves From Drmentioning

confidence: 99%

“…Prior to statistical analysis, the data were binned using the adaptive binning procedure of Davis et al (2007). Briefly, this method identifies the range over which peak positions vary within the 1D NMR dataset and applies these ranges to be the start and end points for data binning.…”

Section: Adaptive Binningmentioning

confidence: 99%

Responses of the pea (Pisum sativum L.) leaf metabolome to drought stress assessed by nuclear magnetic resonance spectroscopy

Charlton¹,

Donarski²,

Harrison³

et al. 2008

Metabolomics

Self Cite

116

View full text Add to dashboard Cite

show abstract

“…A recent kernelbased method of binning seeks to optimally reduce variable count while retaining spectral information by applying a Gaussian weighting function [57]. Other adaptive binning methods rely on a recursive algorithm [56], undecimated wavelet transforms [58] or the optimization of an objective function using a dynamic programming strategy [59] to identify bin edges. Regardless of the approach, adaptive binning performs significantly better than uniform binning [59].…”

Section: Binning and Alignmentmentioning

confidence: 99%

Multivariate Analysis in Metabolomics

Worley¹,

Powers²

2012

CMB

322

388

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Metabolomics aims to provide a global snapshot of all small-molecule metabolites in cells and biological fluids, free of observational biases inherent to more focused studies of metabolism. However, the staggeringly high information content of such global analyses introduces a challenge of its own; efficiently forming biologically relevant conclusions from any given metabolomics dataset indeed requires specialized forms of data analysis. One approach to finding meaning in metabolomics datasets involves multivariate analysis (MVA) methods such as principal component analysis (PCA) and partial least squares projection to latent structures (PLS), where spectral features contributing most to variation or separation are identified for further analysis. However, as with any mathematical treatment, these methods are not a panacea; this review discusses the use of multivariate analysis for metabolomics, as well as common pitfalls and misconceptions.

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“…Thus taking more bins may make classification more difficult. In the case of NMR data, where limited peak shifts are expected between samples, bins can be placed automatically and, depending on the amount of smoothing performed, can be related directly to peaks or to groups of peaks [41]. A reference signal is obtained by taking the maximum at each data point over all the samples and smoothed using wavelet methods.…”

Section: Discussionmentioning

confidence: 99%

Data processing in metabolic fingerprinting by CE‐UV: Application to urine samples from autistic children

Soria

Wright²,

Goodall

et al. 2007

Electrophoresis

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Data processing in metabolic fingerprinting by CE-UV: Application to urine samples from autistic childrenMetabolic fingerprinting of biofluids such as urine can be used to detect and analyse differences between individuals. However, before pattern recognition methods can be utilised for classification, preprocessing techniques for the denoising, baseline removal, normalisation and alignment of electropherograms must be applied. Here a MEKC method using diode array detection has been used for high-resolution separation of both charged and neutral metabolites. Novel and generic algorithms have been developed for use prior to multivariate data analysis. Alignment is achieved by combining the use of reference peaks with a method that uses information from multiple wavelengths to align electropherograms to a reference signal. This metabolic fingerprinting approach by MEKC has been applied for the first time to urine samples from autistic and control children in a nontargeted and unbiased search for markers for autism. Although no biomarkers for autism could be determined using MEKC data here, the general approach presented could also be applied to the processing of other data collected by CE with UV-Vis detection. Keywords IntroductionMetabolic fingerprinting approaches [1] have recently emerged as a nonbiased alternative to the determination of individual levels of a specific metabolite or class of metabolites in large sets of samples, as is necessary for clinical diagnosis purposes. 1 H-NMR has been widely used in urinary fingerprinting and the search for disease biomarkers [2,3]. The approach has been recently complemented by chromatographic techniques hyphenated to MS (GC-MS and LC-MS), with the advantage over 1 H-NMR of a higher sensitivity [4][5][6][7]. Other spectroscopic techniques used less frequently for urinary fingerprinting include direct injection MS [8,9] and FTIR spectroscopy [10]. CE has recently been incorporated to the family of metabolic fingerprinting techniques used for clinical studies [11][12][13][14]. CE is an efficient and sensitive high-resolution separation technique that allows fast analysis of ionic species and the use of small samples.MEKC, applicable to both ionic and neutral species, has proved to be a useful tool in the study of different target metabolites in urine [15][16][17][18]. However, its application as a nontargeted approach has not yet been fully exploited. CD-modified MEKC allows the separation of over 80 UVabsorbing endogenous analytes of different charge types (neutral, anionic and cationic) below 25 min [18][19][20]. Since minimal sample pretreatment is required, this methodology has the potential for high-throughput analysis of the large number of samples required in screening applications.Urinary fingerprints produced by MEKC are highly complex datasets and require multivariate techniques for interpretation. Unsupervised pattern recognition methods such as cluster analysis and principal component analysis (PCA) have been used to examine the structure of these dataset...

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Adaptive binning: An improved binning method for metabolomics data using the undecimated wavelet transform

Cited by 94 publications

References 18 publications

Responses of the pea (Pisum sativum L.) leaf metabolome to drought stress assessed by nuclear magnetic resonance spectroscopy

Responses of the pea (Pisum sativum L.) leaf metabolome to drought stress assessed by nuclear magnetic resonance spectroscopy

Multivariate Analysis in Metabolomics

Data processing in metabolic fingerprinting by CE‐UV: Application to urine samples from autistic children

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