代謝組學(xué)介紹PPT課件

1、Introduction of metabonomics/metabolomics,2009-06-26,The flow of the “omics” sciences: genomics, proteomics, and metabolomics,Spratlin J.L. , et al. Clin Cancer Res, 2009 January 15, 15(2):431-440,Whats in a name?,Metabolome “ refers to the complete set of small-molecule metabolites (such as metabol

2、ic intermediates, hormones and other signalling molecules, and secondary metabolites) to be found within a biological sample, such as a single organism ” Oliver et al., 1998 代謝組“是指基因組的所有下游產(chǎn)物也即最終產(chǎn)物的組合，這些產(chǎn)物是一些參與生物新陳代謝、維持生物體正常功能和生長發(fā)育的小分子化合物，主要是相對分子量小于1000Da的內(nèi)源性小分子”,許國旺著. 代謝組學(xué)-方法與應(yīng)用, 科學(xué)出版社，2008年第一版:第一章,

3、P1-10,Metabonomics “measurement of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification” Nicholson et al., 1999 Metabolomics “.the complete set of metabolites/low-molecular-weight intermediates, which are context dependent, varying ac

4、cording to the physiology, developmental or pathological state of the cell, tissue, organ or organism” Oliver 2002 代謝組學(xué)“是通過考察生物體系（細(xì)胞、組織或生物體）受到刺激或擾動后（如將某個(gè)特定的基因變異或者環(huán)境變化后），其代謝產(chǎn)物的變化或其隨時(shí)間的變化，來研究生物體系的一門科學(xué)” 許國旺 2008,Whats in a name?,Analytical plat-forms： (1) Nuclear magnetic resonance (NMR); (2) Gas Chrom

5、atographyMass Spectrometry ( GC-MS ); (3) Liquid Chromatography-Mass Spectrometry ( LC-MS ); etc.,Metadata obtain,Tao X.M., et al. Anal Bioanal Chem., 2008, 391:2881-2889,Total ion chromatogram,Data obtain (1) Filtering and peak detection 濾噪、峰檢測 (2) Deconvolution 重疊峰解析 （3）Peak alignment 峰對齊、匹配 （4）No

6、rmalization 歸一化,Data analysis and interpretation (5) 非監(jiān)督的模式識別方法： 利用獲取的樣本信息，對樣本進(jìn)行歸類，并采用相應(yīng)的可視化技術(shù)直觀的表達(dá)出來，不需要有關(guān)樣品分類的任何背景信息。該方法將得到的分類信息和這些樣本的原始信息（如疾病的種）進(jìn)行比較，建立代謝產(chǎn)物與這些原始信息的聯(lián)系，篩選與原始信息相關(guān)的標(biāo)志物，進(jìn)而考察其中的代謝途徑。 常用的非監(jiān)督學(xué)習(xí)方法如主成分分析(principal components analysis), 系統(tǒng)聚類分析,主成分分析的基本思想： 對變量X進(jìn)行線性變換，形成新的綜合變量PC；根據(jù)實(shí)際需要選擇2-3個(gè)PC

7、進(jìn)行分析，以達(dá)到降維和簡化問題的作用（多元 二元/三元） PC1=a11X1+a21X2+ +ap1Xp PC2=a12X1+a22X2+ +ap2Xp,許國旺等著. 代謝組學(xué)-方法與應(yīng)用, 科學(xué)出版社，2008年第一版:第12章,146-156,PCA scores plot of onset ALL and AML patients,Data analysis (6) 有監(jiān)督的模式識別方法： 利用一組已知分類的樣本作為訓(xùn)練集，讓計(jì)算機(jī)對其進(jìn)行學(xué)習(xí)，獲取分類的基本模型，進(jìn)而可以利用這種模型對另一組分類未知的樣本進(jìn)行類別識別。 常用的有監(jiān)督學(xué)習(xí)方法如偏最小二乘判別分析(Partial leas

8、t squares-discriminant analysis，PLS-DA)，正交偏最小二乘判別分析，費(fèi)舍爾線性判別分析,許國旺等著. 代謝組學(xué)-方法與應(yīng)用, 科學(xué)出版社，2008年第一版:12,146-156,偏最小二乘法分析思想 對變量進(jìn)行分類：設(shè)定p個(gè)因變量Y1, , Yp和m個(gè)自變量X1, ，Xm，對兩類變量進(jìn)行建模。提取自變量的第一成分T1和因變量的第一成分U1，使T1和U1相關(guān)程度達(dá)最大, 然后建立U1和T1的回歸方程；如果回歸方程未達(dá)到滿意的精度，則用同樣的方法提取T2和U2。 T1=w11X1+ +w1mXm T2=w21X1+ +w2mXm,判別分析思想 應(yīng)變量為定性變量，

9、且分組類型在兩組以上；自變量為可測量的度量變量。計(jì)算（線性）判別式；將自變量代入判別式，計(jì)算每個(gè)觀察樣本的判別Z得分，然后根據(jù)得分值對其進(jìn)行歸類。,The scores t, one vector for each model dimension, are new variables computed as linear combinations of the Xs. They provide a summary of X that both approximate X and predict Y.,PLS-DA scores plot of onset ALL and AML patient

10、s,Other statistic approaches, such as t test and ANOVA, are alternatives at this step.,VIP (variable importance in the projection ) values,The influence of every term in the matrix X on all the Ys. VIP is normalized so that Sum (VIP)2 = K (number of terms in the matrix X). Terms with VIP 1 have an a

11、bove average influence on Y.,Deviation of each variables from ALL (standard deviations from average),Potential biomarker identification: standard Students t test or ANOVA,Blind prediction test of PLSDA model,Y-Predicted,Three major steps of metabolomics analysis,Spratlin J.L. , et al. Clin Cancer Re

12、s, 2009 January 15, 15(2):431-440,Clinical applications of metabolomics in oncology,1. Search early diagnostic biomarkers,Breast cancer: tCho glycerophosphocholine glucose,2. Response assessment to chemical drugs/therapy treatments,Both as a predictive measure of efficacy and a pharmacodynamic marke

13、r,Tiziani S, Lodi A, Khanim FL, Viant MR, Bunce CM, et al. PLoS ONE, 2009, 4(1):e4251,Bathen TF, et al. Breast Cancer Res Treat, 2007;104:181189.,Some knowledge about prostate cancer,1. Prostate cancer the most frequently diagnosed cancer in men,2. current diagnostic methods: using a combination of

14、digital rectal examination and measuring the levels of the enzyme PSA in the blood serum,3. limitation of current diagnosis: the features of this kind of cancer are notoriously variable among patients.,Metabolomic profiling of prostate cancer,Screekumar A., et al. Nature, 2009 Feruary 12, 457(7231):

15、910-914,a, Venn diagram of the total metabolites detected across 42 prostate-related tissues and 110 matched plasma and urine samples. b, Venn diagram of 626 metabolites in tissues measured across 16 benign adjacent prostate tissues, 12 clinically localized prostate cancers (PCA) and 14 metastatic p

16、rostate cancers (Mets),Screekumar A., et al. Nature, 2009 Feruary 12, 457(7231):910-914,Metabolomic profiling of prostate cancer,Screekumar A., et al. Nature, 2009 Feruary 12, 457(7231):910-914,Hierarchical cluster analysis of prostate tissue samples,Screekumar A., et al. Nature, 2009 Feruary 12, 45

17、7(7231):910-914,blue circles-benign adjacent prostate,yellow squares-localized prostate cancer,red triangles-metastatic prostate cancer,Principal components analysis of prostate tissue samples,Screekumar A., et al. Nature, 2009 Feruary 12, 457(7231):910-914,blue-benign; yellow-localized,two-tailed Wilco