Metabolomics - CERM
Metabolomics - CERM Metabolomics - CERM
PCA Details/Advice In some cases PCA will not succeed in identifying any clear clusters or obvious groupings no matter how many components are used. If this is the case, it is wise to accept the result and assume that the presumptive classes or groups cannot be distinguished with PCA As a general rule, if a PCA analysis fails to achieve even a modest separation of classes, then it is probably better to use other statistical techniques to try to separate them
SIMCA Supervised learning method based on PCA Construct a seperate PCA model for each known class of observations PCA models used to assign the class belonging to observations of unknown class origin Recommended for use in one class case or for classification if no interpretation is needed CLASS SPECIFIC STUDIES One-class problem: Only disease observations define a class; control samples are too heterogeneous, for example, due to other variations caused by diseases, gender, age, diet, lifestyle, etc. Two-class problem: Disease and control observations define two seperate classes
- Page 1 and 2: Metabolomics Leonardo Tenori FiorGe
- Page 3 and 4: “La metabolomica è l’ultima na
- Page 5 and 6: Metabolomica: la nuova frontiera
- Page 7 and 8: Metabonomics “…measurement of t
- Page 9 and 10: Metabolomica: alcuni obiettivi Valu
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- Page 13 and 14: The metabolome consists of what? Sm
- Page 15 and 16: C om p ound class N u m b er C om p
- Page 17 and 18: Why 1 µM? Equals ~200 ng/mL Limit
- Page 19 and 20: Medical Metabolomics Generate metab
- Page 21 and 22: Traditional Metabolite Analysis HPL
- Page 23 and 24: What’s the Difference Between Met
- Page 25 and 26: Advantages Measure multiple (10’s
- Page 27 and 28: 2 Routes to Metabolomics Two approa
- Page 29 and 30: Benefits of analyzing the metabolom
- Page 31 and 32: Advantages: Use of NMR in Non-destr
- Page 33 and 34: Raccolta e stoccaggio dei campioni
- Page 35 and 36: NMR A typical 950-MHz H NMR spectru
- Page 37 and 38: Profilo 1 H NMR di urina umana
- Page 39 and 40: Profilo di siero umano Proteins + L
- Page 41 and 42: Data analysis - approach Classify N
- Page 43: PCA Unsupervised Multivariate analy
- Page 51: PCA Loadings Plot • Loadings plot
- Page 55 and 56: OPLS OPLS method is a recent modifi
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- Page 59 and 60: Databases
- Page 61 and 62: Effetto della dieta Trimetilammina
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- Page 65 and 66: METabolomic REFerence The METREF pr
- Page 67 and 68: Ind 1 Ind 2 METabolomic REFerence G
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- Page 75 and 76: METabolomic REFerence 2 2005 collec
- Page 77 and 78: Un “salto” metabolico Evoluzion
- Page 79 and 80: Celiac Disease Metabolomics Experim
- Page 81 and 82: Celiac Disease Metabolomics Note: b
- Page 83 and 84: Celiac Disease Metabolomics Celiac
- Page 85 and 86: Celiac Disease Metabolomics Cluster
- Page 87 and 88: Potential celiac disease Subjects:
- Page 89 and 90: Celiachia Potenziale Celiaci - Sani
- Page 91 and 92: ACIDO LINOLENICO REGIONE DEI METILI
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PCA Details/Advice<br />
In some cases PCA will not succeed in<br />
identifying any clear clusters or obvious<br />
groupings no matter how many components<br />
are used. If this is the case, it is wise to<br />
accept the result and assume that the<br />
presumptive classes or groups cannot be<br />
distinguished with PCA<br />
As a general rule, if a PCA analysis fails to<br />
achieve even a modest separation of classes,<br />
then it is probably better to use other<br />
statistical techniques to try to separate them