Bioinformatics for Biologists
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1 Bioinformatics for Biologists Microarray Data Analysis. Lecture 1. Fran Lewitter, Ph.D. Director Bioinformatics and Research Computing Whitehead Institute
2 Outline Introduction Working with microarray data Normalization Analysis Distance metrics Clustering methods WIBR Microarray Course, Whitehead Institute, May
3 Research Trends Genomics How are genes regulated? How do genes interact? Sequence What are the functional roles of different genes? Function How does expression level of a gene differ in different tissues? WIBR Microarray Course, Whitehead Institute, May
4 Transcriptional Profiling (Adapted from Quackenbush 2001) Study of patterns of gene expression across many experiments that survey a wide array of cellular responses, phenotypes and conditions Simple analysis - what s up/down regulated? More interesting - identify patterns of expression for insight into function, etc. WIBR Microarray Course, Whitehead Institute, May
5 Microarray Data Collect data on n DNA samples (e.g. rows, genes, promotors, exons, etc.) for p mrna samples of tissues or experimental conditions (eg. columns, time course, pathogen exposure, mating type, etc) x 11 x 12. x 1p Matrix (n x p) = x 21 x 22. x 2p.... x n1 x n2. x np WIBR Microarray Course, Whitehead Institute, May
6 Multivariate Analysis Concerned with datasets with more than one response variable for each observational or experimental unit (e.g. matrix X with n rows (genes) and p columns (tissue types)) Hierarchical (phylogenetic trees) vs non-hierarchical (kmeans) Divisive vs agglomerative Supervised vs unsupervised Divide cases into groups vs discover structure of data WIBR Microarray Course, Whitehead Institute, May
7 Multivariate Methods Cluster analysis - discover groupings among cases of X Hierarchical produces dendograms K-means - choose a prespecified number of clusters Self Organizing Maps Principal component analysis (PCA) Linear method, unsupervised, seeks linear combinations of the columns of X with maximal (or minimal) variance (graphical) WIBR Microarray Course, Whitehead Institute, May
8 DNA Microarrays Build the chip Prepare RNA Hybridize array Collect results Normalize Analyze WIBR Microarray Course, Whitehead Institute, May
9 Data Normalization Correct for systematic bias in data Avoid it, recognize it, correct it, discard outliers First step for comparing data from one array to another WIBR Microarray Course, Whitehead Institute, May
10 Sources of variation wanted vs unwanted Across experimental conditions Chip, slide Hybridization conditions Imaging WIBR Microarray Course, Whitehead Institute, May
11 Normalization Approaches Compensate for experimental variability Housekeeping genes Spiked in controls Global median normalization Total intensity normalization LOWESS correction WIBR Microarray Course, Whitehead Institute, May
12 Expression Ratios Let R = a query sample Let G = a reference sample Then the ratio, T i = R i /G i Need to transform these to log 2 Examples: T = 2/1 = 2; T=1/2 =.5 Examples: log 2 (2) = 1; log 2 (.5) = -1 WIBR Microarray Course, Whitehead Institute, May
13 Total Intensity Normalization (Adapted from Quackenbush 2002) Assumptions: (1) start with equal amounts of RNA for the two samples; (2) arrayed elements represent random sample of genes in the organism a. c. b. Rescale intensities: d. WIBR Microarray Course, Whitehead Institute, May
14 LOWESS - The R-I Plot (Adapted from Quackenbush 2002) Data exhibit an intensity-dependent structure Uncertainty in intensity and ratio measurements is greater at lower intensities WIBR Microarray Course, Whitehead Institute, May
15 LOWESS - The R-I Plot (Adapted from Quackenbush 2002) Plot log 2 (R/G) ratio as a function of log 10 (R*G) product intensity Shows intensity specific artifacts in the measurements of ratios Correct using a local weighted linear regression WIBR Microarray Course, Whitehead Institute, May
16 LOWESS Normalization (From Quackenbush 2002) WIBR Microarray Course, Whitehead Institute, May
17 After normalization (Adapted from Quackenbush 2001) Data reported as an expression ratio or as a logarithm of the expression ratio Expression ratio is the normalized value of the expression level for a particular gene in the query sample divided by its normalized value for the control Use log of expression ratio for easier comparisons WIBR Microarray Course, Whitehead Institute, May
18 Citations Brazma A and Vilo J. Minireview: Gene expression data analysis. FEBS Letters 480:17-24, Quackenbush J. Computational Analysis of Microarray Data. Nature Review Genetics 2: , Quackenbush J. Microarray data normalization and transformation. Nature Genetics Supp. 32: , Dudoit S and Gentleman R. Classification in microarray experiments. Statistics and Genomics Short Course - Lecture 5, January 2002 ( WIBR Microarray Course, Whitehead Institute, May
19 Lists of Tools Local WI Page WADE R Statistics Package Microarray Tools Bioconductor Project EBI WIBR Microarray Course, Whitehead Institute, May
20 Exercise 1 Excel Conventions A2 A2:A100 =B5 =$B$5 =data!b4 =[otherfile.xls]data!b4 cell reference series of cells formula absolute link reference other sheet reference other file WIBR Microarray Course, Whitehead Institute, May
21 Exercise 1 Functions MEDIAN SUM AVERAGE IF TTEST VLOOKUP WIBR Microarray Course, Whitehead Institute, May
22 Exercise 1 To Do Affy - fetal & human adult liver & brain tissue Normalize data - 8 chips (replicates) Global median normalization (expression signal/chip median value)*100 Filter low intensity signals Based on A/P Eliminate signal similar to background Calculate ratios Reduce data (replicates) Use AVERAGE function Ratio of fetal tissue/adult tissue Log 2 WIBR Microarray Course, Whitehead Institute, May
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