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A correction method for systematic error in 1 H-NMR time-course data validated through stochastic cell culture simulation

Overview of attention for article published in BMC Systems Biology, January 2015
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  • Above-average Attention Score compared to outputs of the same age (61st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (57th percentile)

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2 tweeters

Citations

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3 Dimensions

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14 Mendeley
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Title
A correction method for systematic error in 1 H-NMR time-course data validated through stochastic cell culture simulation
Published in
BMC Systems Biology, January 2015
DOI 10.1186/s12918-015-0197-4
Pubmed ID
Authors

Stanislav Sokolenko, Marc G Aucoin, Sokolenko, Stanislav, Aucoin, Marc G

Abstract

The growing ubiquity of metabolomic techniques has facilitated high frequency time-course data collection for an increasing number of applications. While the concentration trends of individual metabolites can be modeled with common curve fitting techniques, a more accurate representation of the data needs to consider effects that act on more than one metabolite in a given sample. To this end, we present a simple algorithm that uses nonparametric smoothing carried out on all observed metabolites at once to identify and correct systematic error from dilution effects. In addition, we develop a simulation of metabolite concentration time-course trends to supplement available data and explore algorithm performance. Although we focus on nuclear magnetic resonance (NMR) analysis in the context of cell culture, a number of possible extensions are discussed. Realistic metabolic data was successfully simulated using a 4-step process. Starting with a set of metabolite concentration time-courses from a metabolomic experiment, each time-course was classified as either increasing, decreasing, concave, or approximately constant. Trend shapes were simulated from generic functions corresponding to each classification. The resulting shapes were then scaled to simulated compound concentrations. Finally, the scaled trends were perturbed using a combination of random and systematic errors. To detect systematic errors, a nonparametric fit was applied to each trend and percent deviations calculated at every timepoint. Systematic errors could be identified at time-points where the median percent deviation exceeded a threshold value, determined by the choice of smoothing model and the number of observed trends. Regardless of model, increasing the number of observations over a time-course resulted in more accurate error estimates, although the improvement was not particularly large between 10 and 20 samples per trend. The presented algorithm was able to identify systematic errors as small as 2.5 % under a wide range of conditions. Both the simulation framework and error correction method represent examples of time-course analysis that can be applied to further developments in (1)H-NMR methodology and the more general application of quantitative metabolomics.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Switzerland 1 7%
Unknown 13 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 29%
Unspecified 3 21%
Student > Ph. D. Student 3 21%
Other 2 14%
Student > Bachelor 2 14%
Other 0 0%
Readers by discipline Count As %
Unspecified 4 29%
Agricultural and Biological Sciences 4 29%
Biochemistry, Genetics and Molecular Biology 2 14%
Chemical Engineering 1 7%
Nursing and Health Professions 1 7%
Other 2 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 March 2016.
All research outputs
#3,092,876
of 7,452,554 outputs
Outputs from BMC Systems Biology
#290
of 812 outputs
Outputs of similar age
#87,681
of 237,529 outputs
Outputs of similar age from BMC Systems Biology
#13
of 35 outputs
Altmetric has tracked 7,452,554 research outputs across all sources so far. This one has received more attention than most of these and is in the 57th percentile.
So far Altmetric has tracked 812 research outputs from this source. They receive a mean Attention Score of 3.2. This one has gotten more attention than average, scoring higher than 60% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 237,529 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.
We're also able to compare this research output to 35 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.