Metabolomics - CERM

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Data analysis and interpretation Data collected represented in a matrix Chemometric Approach Principle Component Analysis (PCA) Soft Independent Modeling of Class Analogy (SIMCA) Partial Least-Squares aka Projections to Latent Structures (PLS) Orthogonal PLS (OPLS) Targeted Profiling
PCA Unsupervised Multivariate analysis based on projection methods Main tool used in chemometrics Extract and display the systematic variation in the data Each Principle Component (PC) is a linear combination of the original data parameters Each successive PC explains the maximum amount of variance possible, not accounted for by the previous PCs PCs Orthogonal to each other Conversion of original data leads to two matrices, known as scores and loadings The scores(T) represent a low-dimensional plane that closely approximates X. Linear combinations of the original variables. Each point represents a single sample spectrum. A loading plot/scatter plot(P) shows the influence (weight) of the individual X-variables in the model. Each point represents a different spectral intensity. The part of X that is not explained by the model forms the residuals(E) X = TP T = t 1p 1 T + t 2p 2 T + ... + E
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
Page 11 and 12: Metabolomica: alcuni obiettivi Scop
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: Data analysis - approach Classify N
Page 51 and 52: PCA Loadings Plot • Loadings plot
Page 53 and 54: SIMCA Supervised learning method ba
Page 55 and 56: OPLS OPLS method is a recent modifi
Page 57 and 58: Targeted profiling Targeted metabol
Page 59 and 60: Databases
Page 61 and 62: Effetto della dieta Trimetilammina
Page 63 and 64: Effetto delle patologie Campioni di
Page 65 and 66: METabolomic REFerence The METREF pr
Page 67 and 68: Ind 1 Ind 2 METabolomic REFerence G
Page 69 and 70: METabolomic REFerence Dendrogram of
Page 71 and 72: METabolomic REFerence An individual
Page 73 and 74: METabolomic REFerence 2 Experimenta
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
Page 93 and 94:
PRODOTTI DI OSSIDAZIONE QUASI ASSEN
Page 95 and 96:
CARATTERIZZAZIONE GEOGRAFICA ( 1 H)
Page 97 and 98:
Metabolomica del latte Recente inte
Page 99 and 100:
Spettro 1 H NMR di marche diverse N
Page 101 and 102:
Formato Spettro 1 H NMR di latte di
Page 103 and 104:
formato Formato acetato e piruvato
Page 105:
Disponibilità di Tesi in METABOLOM
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Metabolomics - CERM

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