Abstract
Principal component analysis (PCA) is routinely applied to the study of NMR based metabolomic data. PCA is used to simplify the examination of complex metabolite mixtures obtained from biological samples that may be composed of hundreds or thousands of chemical components. PCA is primarily used to identify relative changes in the concentration of metabolites to identify trends or characteristics within the NMR data that permits discrimination between various samples that differ in their source or treatment. A common concern with PCA of NMR data is the potential over emphasis of small changes in high concentration metabolites that would over-shadow significant and large changes in low-concentration components that may lead to a skewed or irrelevant clustering of the NMR data. We have identified an additional concern, very small and random fluctuations within the noise of the NMR spectrum can also result in large and irrelevant variations in the PCA clustering. Alleviation of this problem is obtained by simply excluding the noise region from the PCA by a judicious choice of a threshold above the spectral noise.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 88-95 |
| Number of pages | 8 |
| Journal | Journal of Magnetic Resonance |
| Volume | 178 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 1 2006 |
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Keywords
- Impact of noise
- Metabolomics
- NMR
- Principal component analysis
ASJC Scopus subject areas
- Biophysics
- Biochemistry
- Nuclear and High Energy Physics
- Condensed Matter Physics
Cite this
Negative impact of noise on the principal component analysis of NMR data. / Halouska, Steven; Powers, Robert.
In: Journal of Magnetic Resonance, Vol. 178, No. 1, 01.01.2006, p. 88-95.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Negative impact of noise on the principal component analysis of NMR data
AU - Halouska, Steven
AU - Powers, Robert
PY - 2006/1/1
Y1 - 2006/1/1
N2 - Principal component analysis (PCA) is routinely applied to the study of NMR based metabolomic data. PCA is used to simplify the examination of complex metabolite mixtures obtained from biological samples that may be composed of hundreds or thousands of chemical components. PCA is primarily used to identify relative changes in the concentration of metabolites to identify trends or characteristics within the NMR data that permits discrimination between various samples that differ in their source or treatment. A common concern with PCA of NMR data is the potential over emphasis of small changes in high concentration metabolites that would over-shadow significant and large changes in low-concentration components that may lead to a skewed or irrelevant clustering of the NMR data. We have identified an additional concern, very small and random fluctuations within the noise of the NMR spectrum can also result in large and irrelevant variations in the PCA clustering. Alleviation of this problem is obtained by simply excluding the noise region from the PCA by a judicious choice of a threshold above the spectral noise.
AB - Principal component analysis (PCA) is routinely applied to the study of NMR based metabolomic data. PCA is used to simplify the examination of complex metabolite mixtures obtained from biological samples that may be composed of hundreds or thousands of chemical components. PCA is primarily used to identify relative changes in the concentration of metabolites to identify trends or characteristics within the NMR data that permits discrimination between various samples that differ in their source or treatment. A common concern with PCA of NMR data is the potential over emphasis of small changes in high concentration metabolites that would over-shadow significant and large changes in low-concentration components that may lead to a skewed or irrelevant clustering of the NMR data. We have identified an additional concern, very small and random fluctuations within the noise of the NMR spectrum can also result in large and irrelevant variations in the PCA clustering. Alleviation of this problem is obtained by simply excluding the noise region from the PCA by a judicious choice of a threshold above the spectral noise.
KW - Impact of noise
KW - Metabolomics
KW - NMR
KW - Principal component analysis
UR - http://www.scopus.com/inward/record.url?scp=28844433017&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=28844433017&partnerID=8YFLogxK
U2 - 10.1016/j.jmr.2005.08.016
DO - 10.1016/j.jmr.2005.08.016
M3 - Article
C2 - 16198132
AN - SCOPUS:28844433017
VL - 178
SP - 88
EP - 95
JO - Journal of Magnetic Resonance
JF - Journal of Magnetic Resonance
SN - 1090-7807
IS - 1
ER -