Gene expression: Microarray data analysis. Copyright notice. Outline: microarray data analysis. Schedule

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1 Gene expression: Microarray data analysis Copyright notice Many of the images in this powerpoint presentation are from Bioinformatics and Functional Genomics by Jonathan Pevsner (ISBN ). Copyright 3 by John Wiley & Sons, Inc. January 5, 9 Jonathan Pevsner, Ph.D. Bioinformatics pevsner@kennedykrieger.org Johns Hopkins School of Medicine (M.E:8.77) These images and materials may not be used without permission from the publisher. We welcome instructors to use these powerpoints for educational purposes, but please acknowledge the source. The book has a homepage at including hyperlinks to the book chapters. Schedule Outline: microarray data analysis Gene expression Today : microarray data analysis (this morning) Today at :3: computer lab (and course review) Tuesday, January 6: no proteomics quiz due Monday, January : final exam 9-:3 am Monday, January : microarray quiz due (noon) Microarrays Preprocessing normalization scatter plots t-test ANOVA Exploratory (descriptive) statistics distances clustering principal components analysis (PCA)

2 Compare gene expression in this cell type Gene expression is context-dependent, and is regulated in several basic ways after viral infection relative to a knockout in samples from patients by region (e.g. brain versus kidney) in development (e.g. fetal versus adult tissue) in dynamic response to environmental signals (e.g. immediate-early response genes) in disease states after drug treatment at a later developmental time in a different body region by gene activity Page 57 UniGene: unique genes via ESTs DNA RNA protein DNA RNA protein Find UniGene at NCBI: UniGene clusters contain many ESTs cdna UniGene SAGE microarray cdna UniGene data come from many cdna libraries. Thus, when you look up a gene in UniGene you get information on its abundance and its regional distribution. next-generation sequencing!!! Fig. 6. Page 59 Page 64

3 Outline: microarray data analysis Microarrays: tools for gene expression Gene expression Microarrays Preprocessing normalization scatter plots A microarray is a solid support (such as a membrane or glass microscope slide) on which DNA of known sequence is deposited in a grid-like array. t-test ANOVA Exploratory (descriptive) statistics distances clustering principal components analysis (PCA) Page 73 Microarrays: tools for gene expression Advantages of microarray experiments Fast Data on >, transcripts in ~ weeks The most common form of microarray is used to measure gene expression. RNA is isolated from matched samples of interest. The RNA is typically converted to cdna, labeled with fluorescence (or radioactivity), then hybridized to microarrays in order to measure the expression levels of thousands of genes. Page 73 Comprehensive Flexible Easy Entire yeast or mouse genome on a chip Custom arrays can be made to represent genes of interest Submit RNA samples to a core facility Cheap? Chip representing, genes for $3 Table 6-4 Page 75 Disadvantages of microarray experiments Cost Some researchers can t afford to do appropriate numbers of controls, replicates RNA The final product of gene expression is protein significance Pervasive transcription of the genome is poorly understood (ENCODE project) There are many noncoding RNAs not yet represented on microarrays Quality control Impossible to assess elements on array surface Artifacts with image analysis Artifacts with data analysis Not enough attention to experimental design Not enough collaboration with statisticians Sample acquisition Data acquisition Data analysis Data confirmation Table 6-5 Page 76 Biological insight Fig. 6.6 Page 76 3

4 Stage : Experimental design Stage : Experimental design Stage : RNA and probe preparation Stage 3: Hybridization to DNA arrays Stage 4: Image analysis Stage 5: Microarray data analysis Stage 6: Biological confirmation Stage 7: Microarray databases Fig. 6.6 Page 76 [] Biological samples: technical and biological replicates: determine the data analysis approach at the outset [] RNA extraction, conversion, labeling, hybridization: except for RNA isolation, routinely performed at core facilities [3] Arrangement of array elements on a surface: randomization can reduce spatially-based artifacts Page 77 One sample per array (e.g. Affymetrix or radioactivity-based platforms) Two samples per array (competitive hybridization) Samples, Samples,3 Samples,3 Sample Sample Sample 3 Sample, pool Sample, pool Samples,: switch dyes Fig. 6.7 Page 77 Fig. 6.7 Page 77 Stage : RNA preparation Stage 3: Hybridization to DNA arrays For Affymetrix chips, need total RNA (about 5 ug) Confirm purity by running agarose gel Measure a6/a8 to confirm purity, quantity One of the greatest sources of error in microarray experiments is artifacts associated with RNA isolation; be sure to create an appropriately balanced, randomized experimental design. Page 78 The array consists of cdna or oligonucleotides Oligonucleotides can be deposited by photolithography The sample is converted to crna or cdna (Note that the terms probe and target may refer to the element immobilized on the surface of the microarray, or to the labeled biological sample; for clarity, it may be simplest to avoid both terms.) Page

5 Microarrays: array surface Stage 4: Image analysis RNA transcript levels are quantitated Fluorescence intensity is measured with a scanner, or radioactivity with a phosphorimager Southern et al. (999) Nature Genetics, microarray supplement Fig. 6.8 Page 79 Page 8 Differential Gene Expression on a cdna Microarray Control Rett α B Crystallin is over-expressed in Rett Syndrome Fig. 6.9 Page 8 Fig. 6. Page 8 Fig. 6. Page 8 Fig. 6. Page 8 5

6 Stage 5: Microarray data analysis Hypothesis testing How can arrays be compared? Which RNA transcripts (genes) are regulated? Are differences authentic? What are the criteria for statistical significance? Clustering Are there meaningful patterns in the data (e.g. groups)? Classification Do RNA transcripts predict predefined groups, such as disease subtypes? Fig. 6. Page 8 Page 8 Stage 6: Biological confirmation Stage 7: Microarray databases Microarray experiments can be thought of as hypothesis-generating experiments. The differential up- or down-regulation of specific RNA transcripts can be measured using independent assays such as -- Northern blots -- polymerase chain reaction (RT-PCR) -- in situ hybridization There are two main repositories: Gene expression omnibus (GEO) at NCBI ArrayExpress at the European Bioinformatics Institute (EBI) Page 8 Page 8 6

7 Array Express at the European Bioinformatics Institute MIAME In an effort to standardize microarray data presentation and analysis, Alvis Brazma and colleagues at 7 institutions introduced Minimum Information About a Microarray Experiment (MIAME). The MIAME framework standardizes six areas of information: experimental design microarray design sample preparation hybridization procedures image analysis controls for normalization Visit Page 8 7

8 Outline: microarray data analysis Microarray data analysis Gene expression Microarrays Preprocessing normalization scatter plots t-test ANOVA Exploratory (descriptive) statistics distances clustering principal components analysis (PCA) begin with a data matrix (gene expression values versus samples) genes (RNA transcript levels) Fig. 7. Page 9 Microarray data analysis Microarray data analysis begin with a data matrix (gene expression values versus samples) begin with a data matrix (gene expression values versus samples) Typically, there are many genes (>>,) and few samples (~ ) Preprocessing Descriptive statistics Fig. 7. Page 9 Fig. 7. Page 9 Microarray data analysis: preprocessing Microarray data analysis: preprocessing Observed differences in gene expression could be due to transcriptional changes, or they could be caused by artifacts such as: different labeling efficiencies of Cy3, Cy5 uneven spotting of DNA onto an array surface variations in RNA purity or quantity variations in washing efficiency variations in scanning efficiency The main goal of data preprocessing is to remove the systematic bias in the data as completely as possible, while preserving the variation in gene expression that occurs because of biologically relevant changes in transcription. A basic assumption of most normalization procedures is that the average gene expression level does not change in an experiment. Page 9 Page 9 8

9 Data analysis: global normalization Global normalization is used to correct two or more data sets. In one common scenario, samples are labeled with Cy3 (green dye) or Cy5 (red dye) and hybridized to DNA elements on a microrarray. After washing, probes are excited with a laser and detected with a scanning confocal microscope. Data analysis: global normalization Global normalization is used to correct two or more data sets Example: total fluorescence in Cy3 channel = 4 million units Cy 5 channel = million units Then the uncorrected ratio for a gene could show, units versus, units. This would artifactually appear to show -fold regulation. Page 9 Page 9 Data analysis: global normalization Scatter plots Global normalization procedure Step : subtract background intensity values (use a blank region of the array) Step : globally normalize so that the average ratio = (apply this to -channel or -channel data sets) Useful to represent gene expression values from two microarray experiments (e.g. control, experimental) Each dot corresponds to a gene expression value Most dots fall along a line Outliers represent up-regulated or down-regulated genes Page 9 Page 93 Brain Differential Gene Expression in Different Tissue and Cell Types Astrocyte Fibroblast Astrocyte Expression level (sample ) low expression level high up Expression level (sample ) down Fig. 7. Page 93 9

10 Scatter plots Typically, data are plotted on log-log coordinates Visually, this spreads out the data and offers symmetry raw ratio log ratio time behavior value value t= basal.. Log-log transformation Fig. 7.3 Page 95 t=h no change.. t=h -fold up.. t=3h -fold down.5 -. Page 94, 97 You can make these plots in Excel low expression level high up Log ratio down Mean log intensity Fig. 7.4 Page 96 but for many bioinformatics applications use R. Visit to download it. M M A A After RMA (a normalization procedure), the median is near zero, and skewing is corrected. Visit to download it. See chapter 9 ( nd edition) for a tutorial on microarray data analysis. Scatterplots display the effects of normalization.

11 SNOMAD converts array data to scatter plots Log-log plot - 4. old -f - - residual.5 - CON CON EXP > CON..5 -fold fold Mean ( Log ( Intensity ) ) EXP < CON Log (Ratio ). robust local regression fit old -f EXP > CON.5 -fold. -fold EXP < CON ld -fo Corrected Log ( Ratio ) [residuals] Log ( Ratio ) EXP EXP -fo ld 3 Linear-linear plot SNOMAD corrects local variance artifacts Mean ( Log ( Intensity ) ) Mean ( Log ( Intensity ) ) Page Robust multi-array analysis (RMA) SNOMAD describes regulated genes in Z-scores Developed by Rafael Irizarry (Dept. of Biostatistics), Terry Speed, and others Available at as an R package Also available in various software packages (including Partek, and Iobion Gene Traffic) See Bolstad et al. (3) Bioinformatics 9; Irizarry et al. (3) Biostatistics 4 Local Log ( Ratio ) Z-Score 5 Corrected Log ( Ratio ) Locally estimated standard deviation of positive ratios -5 Z= -fold Z= - -fold Z= Mean ( Log ( Intensity ) ) Z= 5.5 Corrected Log ( Ratio ) -.5 There are three steps: Locally estimated standard deviation of negative ratios - -. Mean ( Log ( Intensity ) ) Z= Z= [] Background adjustment based on a normal plus exponential model (no mismatch data are used) [] Quantile normalization (nonparametric fitting of signal intensity data to normalize their distribution) [3] Fitting a log scale additive model robustly. The model is additive: probe effect + sample effect -fold -fold Z= - - Z= -5 Z= - Z= Mean ( Log ( Intensity ) ) log intensity 8 RMA adjusts for the effect of GC content log signal intensity Histograms of raw intensity values for 4 arrays (plotted in R) before and after RMA was applied. 4 array GC content 6 4 log signal intensity Page array

12 Robust multi-array analysis (RMA) precision accuracy precision with accuracy RMA offers a large increase in precision (relative to Affymetrix MAS 5. software). precision Good performance: reproducibility of the result Good quality of the result (relative to a gold standard) log expression SD MAS 5. RMA average log expression Robust multi-array analysis (RMA) RMA offers comparable accuracy to MAS 5.. Outline: microarray data analysis Gene expression observed log expression accuracy log nominal concentration Microarrays Preprocessing normalization scatter plots t-test ANOVA Exploratory (descriptive) statistics distances clustering principal components analysis (PCA) are used to make inferences about a population from a sample. Hypothesis testing is a common form of inferential statistics. A null hypothesis is stated, such as: There is no difference in signal intensity for the gene expression measurements in normal and diseased samples. The alternative hypothesis is that there is a difference. We use a test statistic to decide whether to accept or reject the null hypothesis. For many applications, we set the significance level α to p <.5. Page 99 A t-test is a commonly used test statistic to assess the difference in mean values between two groups. x x difference between mean values t = = SE variability (standard error of the difference) Questions Is the sample size (n) adequate? Are the data normally distributed? Is the variance of the data known? Is the variance the same in the two groups? Is it appropriate to set the significance level to p <.5? Page 99

13 A t-test is a commonly used test statistic to assess the difference in mean values between two groups. x x difference between mean values t = = SE variability (standard error of the difference) Notes t is a ratio (it thus has no units) We assume the two populations are Gaussian The two groups may be of different sizes Obtain a P value from t using a table For a two-sample t test, the degrees of freedom is N -. For any value of t, P gets smaller as df gets larger Analyzing expression data in Excel Question: for each of my, transcripts, decide whether it is significantly regulated in some disease. control disease [] Obtain a matrix of genes (rows) and expression values columns. Here there are, rows of genes of which the first six are shown. There are three control samples and three disease samples. You can also calculate the mean value for each gene (transcript) for the controls and the disease (experimental) samples. Analyzing expression data in Excel Analyzing expression data in Excel [] You can calculate the ratios of control versus disease. Note that you can use the formula =E5/I5 in this case to divide the mean control and disease values. Also note that some ratios, such as., appear to be dramatic while others are not. Some researchers set a cutoff for changes of interest such as two-fold. [3] Perform a t-test. When you enter =TTEST into the function box above, a dialog box appears. Enter the range of values for controls and for disease samples, and specify a - or -tailed test. Analyzing expression data in Excel Analyzing expression data in Excel [3] Perform a t-test (continued). For a one-tailed test, your prior hypothesis is that the transcript in the disease group is up (or down) relative to controls; the change is unidirectional. For example, in Down syndrome samples you might hypothesize that chromosome transcripts are significantly up-regulated because of the extra copy of chromosome. [3] Perform a t-test (continued). For a two-tailed test, you hypothesize that the two groups are different, but you do not know in which direction. 3

14 Analyzing expression data in Excel t-test to determine statistical significance disease vs normal [3] Note the results: you can have a small p value (<.5) with a big ratio difference a small p value (<.5) with a trivial ratio difference a large p value (>.5) with a big ratio difference a large p value (>.5) with a trivial ratio difference Only the first group is worth reporting! Why? Error t statistic = difference between mean of disease and normal variation due to error ANOVA partitions total data variability Before partitioning After partitioning Paradigm Parametric test Nonparametric Error disease vs normal Subject Error disease vs normal Tissue type Compare two unpaired groups Unpaired t-test Mann-Whitney test Compare two paired groups Paired t-test Wilcoxon test F ratio = variation between DS and normal variation due to error Compare 3 or more groups ANOVA Table 7- Page 98- Is it appropriate to set the significance level to p <.5? If you hypothesize that a specific gene is up-regulated, you can set the probability value to.5. You might measure the expression of, genes and hope that any of them are up- or down-regulated. But you can expect to see 5% (5 genes) regulated at the p <.5 level by chance alone. To account for the thousands of repeated measurements you are making, some researchers apply a Bonferroni correction. The level for statistical significance is divided by the number of measurements, e.g. the criterion becomes: p < (.5)/, or p < 5 x -6 The Bonferroni correction is generally considered to be too conservative. Page 99 : false discovery rate The false discovery rate (FDR) is a popular multiple corrections correction. A false positive (also called a type I error) is sometimes called a false discovery. The FDR equals the p value of the t-test times the number of genes measured (e.g. for, genes and a p value of., there are expected false positives). You can adjust the false discovery rate. For example: FDR # regulated transcripts # false discoveries Would you report regulated transcripts of which are likely to be false positives, or transcripts of which one is likely to be a false positive? 4

15 A volcano plot displays both p values and fold change p value (treated versus control) log fold change (treated/untreated) Outline: microarray data analysis Gene expression Microarrays Preprocessing normalization scatter plots t-test ANOVA Exploratory (descriptive) statistics distances clustering principal components analysis (PCA) Descriptive statistics Microarray data are highly dimensional: there are many thousands of measurements made from a small number of samples. Descriptive (exploratory) statistics help you to find meaningful patterns in the data. A first step is to arrange the data in a matrix. Next, use a distance metric to define the relatedness of the different data points. Two commonly used distance metrics are: -- Euclidean distance -- Pearson coefficient of correlation Page 3 5

16 What is a cluster? samples (time points) A cluster is a group that has homogeneity (internal cohesion) and separation (external isolation). The relationships between objects being studied are assessed by similarity or dissimilarity measures. genes Data matrix ( genes and 3 time points from Chu et al., 998) Software: S- PLUS package Fig. 7.8 Page 5 Descriptive statistics: clustering Clustering algorithms offer useful visual descriptions of microarray data. t=. t=.5 t= 3D plot (using S-PLUS software) Fig. 7.8 Page 5 Genes may be clustered, or samples, or both. We will next describe hierarchical clustering. This may be agglomerative (building up the branches of a tree, beginning with the two most closely related objects) or divisive (building the tree by finding the most dissimilar objects first). In each case, we end up with a tree having branches and nodes. Page 4 Agglomerative clustering Agglomerative clustering a b a,b a b a,b c c d e d e d,e Adapted from Kaufman and Rousseeuw (99) Fig. 7.9 Page 6 Fig. 7.9 Page 6 6

17 Agglomerative clustering Agglomerative clustering a b c d e a,b d,e c,d,e a b c d e a,b d,e c,d,e a,b,c,d,e tree is constructed Fig. 7.9 Page 6 Fig. 7.9 Page 6 Divisive clustering Divisive clustering a,b,c,d,e a,b,c,d,e c,d,e Fig. 7.9 Page 6 Fig. 7.9 Page 6 Divisive clustering c,d,e a,b,c,d,e a,b Divisive clustering c,d,e a,b,c,d,e d,e d,e Fig. 7.9 Page 6 Fig. 7.9 Page 6 7

18 agglomerative a b c d e a,b d,e Divisive clustering c,d,e a,b,c,d,e a b c d e a,b d,e 3 c,d,e 4 a,b,c,d,e 4 3 tree is constructed Fig. 7.9 Page Adapted from Kaufman and Rousseeuw (99) divisive Fig. 7.9 Page 6 Fig. 7.8 Page 5 Fig. 7. Page 7 Cluster and TreeView Agglomerative and divisive clustering sometimes give conflicting results, as shown here Fig. 7. Page 7 Fig. 7. Page 8 8

19 Cluster and TreeView Cluster and TreeView clustering K means SOM PCA Fig. 7. Page 8 Fig. 7. Page 8 Two-way clustering of genes (y-axis) and cell lines (x-axis) (Alizadeh et al., ) Principal components analysis (PCA) An exploratory technique used to reduce the dimensionality of the data set to D or 3D For a matrix of m genes x n samples, create a new covariance matrix of size n x n Thus transform some large number of variables into a smaller number of uncorrelated variables called principal components (PCs). Fig. 7.3 Page 9 Page Principal components analysis (PCA): objectives to reduce dimensionality to determine the linear combination of variables to choose the most useful variables (features) to visualize multidimensional data to identify groups of objects (e.g. genes/samples) to identify outliers Page Page 9

20 Page Page Page Fig. 7.6 Page Fig. 7.6 Page Fig. 7.6 Page

21 Outline: microarray data analysis Gene expression Microarrays Preprocessing normalization scatter plots t-test ANOVA Exploratory (descriptive) statistics distances clustering principal components analysis (PCA)