发明名称 |
DATA CORRECTION, NORMALIZATION AND VALIDATION FOR QUANTITATIVE HIGH-THROUGHPUT METABOLOMIC PROFILING |
摘要 |
<p>Metabolomic profiling of a biological sample using a separation-molecular ID process, such as gas chromatograpliy-mass spectrometry ("GC-MS"), requires the derivatization of the original sample. Quantitative GC-MS metabolomics is possible if the derivative is in one-to-one proportional relationship with the original concentration profile, wherein the proportionality remaining constant among samples. Two types of biases may be introduced into determination of a metabolomic profile to alter these conditions. The first type of bias is produced by a change in the proportionality size between profiles and is corrected by way of an internal standard. The second type of bias may distort the one-to-one relationship and change the proportionality between the profiles to a different fold-extent for each metabolite in a sample. The metabolomic profile data is corrected from these biases to reduce the risk of assigning biological significance to changes due only to chemical kinetics. A data correction and validation strategy provides for a weighted average of metabolite derivatives after derivatization of an original metabolite and before steady state equilibrium is established between plural metabolite derivatives to maintain high-throughput data acquisition and metabolomics analysis.</p> |
申请公布号 |
WO2007008307(A2) |
申请公布日期 |
2007.01.18 |
申请号 |
WO2006US21317 |
申请日期 |
2006.05.31 |
申请人 |
KANANI, HARIN;KLAPA, MARIA, I. |
发明人 |
KANANI, HARIN;KLAPA, MARIA, I. |
分类号 |
G06F19/00 |
主分类号 |
G06F19/00 |
代理机构 |
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