Negative impact of noise on the principal component analysis of NMR data

Halouska, Steven M; Powers, Robert

doi:10.1016/j.jmr.2005.08.016

Cited by 73 publications

(71 citation statements)

References 24 publications

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“…A principal disadvantage of data scaling is its tendency to amplify instrumental noise, to which PCA and PLS have been shown to be sensitive [52,83]. Methods of scaling based on Maximum Likelihood PCA (MLPCA) [84] have been used to estimate and remove instrumental errors prior to multivariate analysis [85].…”

Section: Noise and Baseline Removalmentioning

confidence: 99%

Multivariate Analysis in Metabolomics

Worley¹,

Powers²

2012

CMB

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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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Section: Noise and Baseline Removalmentioning

confidence: 99%

Multivariate Analysis in Metabolomics

Worley¹,

Powers²

2012

CMB

Self Cite

322

388

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“…After Fourier transformation and phase correction, residual water, and buffer peaks were removed from the spectra, the entire one-dimensional 1 H NMR spectra were normalized using center averaging and binned using intelligent bucketing. Noise regions were eliminated and then the bins were scaled using center averaging prior to principal component analysis (PCA) and orthogonal projection to latent structures discriminate analysis (37).…”

Section: Determination Of Transcriptional Start Sites Of Nmmn_0640mentioning

confidence: 99%

Catabolite Control Protein E (CcpE) Is a LysR-type Transcriptional Regulator of Tricarboxylic Acid Cycle Activity in Staphylococcus aureus

Hartmann

Zhang

Baronian

et al. 2013

Journal of Biological Chemistry

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Background:The TCA cycle is a central metabolic pathway that facilitates the adaption of bacteria to a nutrient-limited environment. Results: Inactivation of CcpE in Staphylococcus aureus resulted in a decreased transcription of the aconitase encoding gene citB, and reduced TCA cycle activity. Conclusion: CcpE affects the TCA cycle via direct transcriptional control of citB. Significance: This is the first positive regulator of TCA cycle activity identified in this pathogen.

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“…The exclusion of noisy dimensions can have a significant impact on the performance of PCA. [21] This weakness of PCA may be overcome by using other feature extraction methods. Sources of noise are the natural inter-and intra-subject gait variability.…”

Section: Discussionmentioning

confidence: 99%

Comparison of different marker sets for marker trajectory and principal component analysis based classification of simulated gait impairments

Christian

Kluge

Eskofier

et al. 2016

JBEI

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Objective: Many different marker sets have been used in marker trajectory based gait classification approaches. Little knowledge exists about the effects of specific marker sets on the subsequent statistical modeling. Such analysis is often based on principal component analysis. The aim of this study was to test the effect of marker set choice on marker trajectory and principal component analysis based gait classification. Methods: This study tested the performance of principal component analysis based gait classification models with various marker sets on the basis of simulated gait impairments. Simulated gait impairments were used to enable a high level of control of the gait patterns. Results: Classification accuracies were similar across most tested marker sets. Improved performance could be detected for some marker sets depending on the type of impairment. Conclusion: Several potentially valid marker sets exist for a specific gait classification task even though trends could be found suggesting that optimal marker set choice is dependent on functional aspects of the movement.

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Negative impact of noise on the principal component analysis of NMR data

Cited by 73 publications

References 24 publications

Multivariate Analysis in Metabolomics

Multivariate Analysis in Metabolomics

Catabolite Control Protein E (CcpE) Is a LysR-type Transcriptional Regulator of Tricarboxylic Acid Cycle Activity in Staphylococcus aureus

Comparison of different marker sets for marker trajectory and principal component analysis based classification of simulated gait impairments

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