02foct22r2

02foct22r2 - Harvard-MIT Division of Health Sciences and...

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1 RNA1: Last week's take home lessons Integration with previous topics (HMM for RNA structure ) Goals of molecular quantitation (maximal fold-changes, clustering & classification of genes & conditions/cell types, causality) Genomics-grade measures of RNA and protein and how we choose (SAGE, oligo-arrays, gene-arrays) Sources of random and systematic errors (reproducibility of RNA source(s), biases in labeling, non-polyA RNAs, effects of array geometry, cross-talk). Interpretation issues (splicing, 5' & 3' ends, editing, gene families, small RNAs, antisense, apparent absence of RNA). Time series data: causality, mRNA decay, time-warping Harvard-MIT Division of Health Sciences and Technology HST.508: Genomics and Computational Biology
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2 RNA2: Today's story & goals • Clustering by gene and/or condition • Distance and similarity measures • Clustering & classification • Applications • DNA & RNA motif discovery & search
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3 Data -Ratios -Log±Ratios - Absolute Measurement - Euclidean Dist. - Manhattan Dist. - Sup. Dist. - Correlation Coeff. -Single -Complete - Average - Centroid Unsupervised | Supervised -SVM - Relevance Networks Hierarchical | Non-hierarchical - Minimal Spanning Tree -K-means -SOM Data Normalization | Distance Metric | Linkage | Clustering Method Gene Expression Clustering Decision Tree How to normalize - Variance normalize - Mean center normalize - Median center normalize What to normaliz e - genes - conditions
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4 (Whole genome) RNA quantitation objectives RNAs showing maximum change minimum change detectable/meaningful RNA absolute levels (compare protein levels) minimum amount detectable/meaningful Classification: drugs & cancers Network -- direct causality-- motifs
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5 Clustering vs. supervised learning K-means clustering SOM = Self Organizing Maps SVD = Singular Value decomposition PCA = Principal Component Analysis SVM = Support Vector Machine classification and Relevance networks Brown et al. PNAS 97:262 Butte et al PNAS 97:12182 (http://www.pnas.org/cgi/content/full/97/1/262) (http://www.pnas.org/cgi/content/full/97/22/12182)
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6 Cluster analysis of mRNA expression data By gene (rat spinal cord development, yeast cell cycle): Wen et al., 1998; Tavazoie et al., 1999; Eisen et al., 1998; Tamayo et al., 1999 (http://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?uid=9419376&form=6&db=m&Dopt=b) (http://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?uid=10391217&form=6&db=m&Dopt=b) (http://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?uid=9843981&form=6&db=m&Dopt=b) (http://www.pubmedcentral.nih.gov/b.cgi?pubmedid=10077610) By condition or cell-type or by gene&cell-type (human cancer): Golub, et al. 1999; Alon, et al. 1999; Perou, et al. 1999; Weinstein, et al 1997 Cheng, ISMB 2000. (http://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?db=m&form=6&uid=10521349&db=m) (http://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?uid=10359783&form=6&Dopt=b) (http://www.pubmedcentral.nih.gov/b.cgi?pubmedid=10430922) . Rana.lbl.gov/EisenSoftware.htm
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7 Cluster Analysis General Purpose: To divide samples into homogeneous groups based on a set of features.
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02foct22r2 - Harvard-MIT Division of Health Sciences and...

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