Gene Expression - Microarrays discusses analyzing gene expression data from microarray experiments. It describes the basic workflow including experimental design, sample preparation, hybridization, image analysis, preprocessing, normalization, and statistical analysis. Key points are that microarrays allow measuring expression of thousands of genes simultaneously, and proper experimental design and data analysis are important to draw meaningful biological conclusions from microarray data.

1. Gene Expression - Microarrays Misha Kapushesky European Bioinformatics Institute, EMBL St. Petersburg, Russia May 2010

6. Compare gene expression in this cell type… … after drug treatment … at a later developmental time … in a different body region … after viral infection … in samples from patients … relative to a knockout

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

9. Microarrays: tools for gene expression 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. Page 312

10. Microarrays: tools for gene expression 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.

11. [email_address] Measuring RNA abundances

18. Fast Data on >20,000 transcripts within weeks Comprehensive Entire yeast or mouse genome on a chip Flexible Custom arrays can be made to represent genes of interest Easy Submit RNA samples to a core facility Cheap? Chip representing 20,000 genes for $300 Advantages of microarray experiments

19. 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 ■ Impossible to assess elements on array surface control ■ Artifacts with image analysis ■ Artifacts with data analysis ■ Not enough attention to experimental design ■ Not enough collaboration with statisticians Disadvantages of microarray experiments

20. Biological insight Sample acquisition Data acquisition Data analysis Data confirmation

21. Stage 1: Experimental design Stage 3: Hybridization to DNA arrays Stage 2: RNA and probe preparation Stage 4: Image analysis Stage 5: Microarray data analysis Stage 6: Biological confirmation Stage 7: Microarray databases

22. Stage 1: Experimental design [1] Biological samples: technical and biological replicates: determine the data analysis approach at the outset [2] 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 314

23. Stage 2: RNA preparation For Affymetrix chips, need total RNA (about 5 ug) Confirm purity by running agarose gel Measure a260/a280 to confirm purity, quantity One of the greatest sources of error in microarray experiments is artifacts associated with RNA isolation; appropriately balanced, randomized experimental design is necessary.

24. Stage 3: Hybridization to DNA arrays 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.)

25. Stage 4: Image analysis RNA transcript levels are quantitated Fluorescence intensity is measured with a scanner.

26. Rett Control Differential Gene Expression on a cDNA Microarray  B Crystallin is over-expressed in Rett Syndrome

28. Fig. 8.21 Page 319

30. Fig. 8.21 Page 319

32. Stage 6: Biological confirmation Page 320 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

33. Stage 7: Microarray databases There are two main repositories: Gene Expression Omnibus (GEO) at NCBI ArrayExpress at the European Bioinformatics Institute (EBI)

34. Microarray Overview I Microbial ORFs Design PCR Primers PCR Products Eukaryotic Genes Select cDNA clones PCR Products For each plate set, many identical replicas Microarray Slide (with 60,000 or more spotted genes) + Microtiter Plate Many different plates containing different genes

35. Microarray Overview II Prepare Fluorescently Labeled Probes Control Test Hybridize, Wash Measure Fluorescence in 2 channels red / green Analyze the data to identify patterns of gene expression

36. Affymetrix GeneChip™ Expression Analysis Obtain RNA Samples Prepare Fluorescently Labeled Probes Control Test Scan chips Analyze PM MM Hybridize and wash chips

37. Gene Microarray Expression Analysis Spots on an Array Fluorescence Intensity Expression Measurement Tissue Selection Differential State/Stage Selection RNA Preparation and Labeling Competitive Hybridization

39. MIAME In an effort to standardize microarray data presentation and analysis, Alvis Brazma and colleagues at 17 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 http://www.mged.org

40. Interpretation of RNA analyses The relationship of DNA, RNA, and protein: DNA is transcribed to RNA. RNA quantities and half-lives vary. There tends to be a low positive correlation between RNA and protein levels. The pervasive nature of transcription: The Encyclopedia of DNA Elements (ENCODE) project identified functional features of genomic DNA, initially in 30 megabases (1% of the human genome). One of its observations was the “pervasive nature of transcription”: the vast majority of DNA is transcribed, although the function is unknown.

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

42. Microarray data analysis • begin with a data matrix (gene expression values versus samples) genes (RNA transcript levels)

43. Microarray data analysis • begin with a data matrix (gene expression values versus samples) Typically, there are many genes (>> 20,000) and few samples ( ~ 10) Fig. 9.1 Page 333

44. Microarray data analysis • begin with a data matrix (gene expression values versus samples) Preprocessing Inferential statistics Descriptive statistics

46. Microarray data analysis: preprocessing 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.

47. 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.

48. 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 = 2 million units Then the uncorrected ratio for a gene could show 2,000 units versus 1,000 units. This would artifactually appear to show 2-fold regulation.

49. Data analysis: global normalization Global normalization procedure Step 1: subtract background intensity values (use a blank region of the array) Step 2: globally normalize so that the average ratio = 1 (apply this to 1-channel or 2-channel data sets)

50. Scatter plots 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

51. Brain Astrocyte Astrocyte Fibroblast Differential Gene Expression in Different Tissue and Cell Types

52. expression level high low up down Expression level (sample 1) Expression level (sample 2)

53. Log-log transformation

54. Scatter plots Typically, data are plotted on log-log coordinates Visually, this spreads out the data and offers symmetry raw ratio log 2 ratio time behavior value value t=0 basal 1.0 0.0 t=1h no change 1.0 0.0 t=2h 2-fold up 2.0 1.0 t=3h 2-fold down 0.5 -1.0

55. expression level high low up down Mean log intensity Log ratio

56. You can make these plots in Excel… … but for many bioinformatics applications use R. Visit http://www.r-project.org to download it.

58. There are limits to what you can measure

59. The Limits of log-ratios: The space we explore

60. The Limits of log-ratios: The space we explore

61. The Limits of log-ratios: The space we explore

62. Good Data

63. Bad Data from Parts Unknown Gary Churchill Each “pin group” is colored differently

65. Ratio Cy3/Cy5 for the same RNA sorted from least most expressed

66. LOWESS Results

67. Affymetrix Chips

76. A A M M After RMA (a normalization procedure), the median is near zero, and skewing is corrected. Scatterplots display the effects of normalization.

78. vsn: post-normalization plot

79. array log signal intensity array log signal intensity Histograms of raw intensity values for 14 arrays (plotted in R) before and after RMA was applied.

80. RMA can adjust for the effect of GC content GC content log intensity

81. Robust multi-array analysis (RMA) RMA offers a large increase in precision (relative to Affymetrix MAS 5.0 software). precision average log expression log expression SD RMA MAS 5.0

82. Robust multi-array analysis (RMA) RMA offers comparable accuracy to MAS 5.0. log nominal concentration observed log expression accuracy

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

84. Inferential statistics Inferential statistics 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  to p < 0.05.

85. [1] Obtain a matrix of genes (rows) and expression values columns. Here there are 20,000 rows of genes of which the first six are shown. There are three control samples and three disease samples. Calculate the mean value for each gene (transcript) for the controls and the disease (experimental) samples. Analyzing expression data Question: for each of my 20,000 transcripts, decide whether it is significantly regulated in some disease. control disease

86. [2] Calculate the ratios of control versus disease. Also note that some ratios, such as 2.00, appear to be dramatic while others are not. Some researchers set a cut-off for changes of interest such as two-fold. Analyzing expression data

87. A significant difference Probably not

88. Inferential statistics A t-test is a commonly used test statistic to assess the difference in mean values between two groups. t = = 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 < 0.05? x 1 – x 2 SE difference between mean values variability (standard error of the difference)

90. [3] Perform a t-test. Hypothesis is that the transcript in the disease group is up (or down) relative to controls. Analyzing expression data

91. [3] Note the results: you can have… a small p value (<0.05) with a big ratio difference a small p value (<0.05) with a trivial ratio difference a large p value (>0.05) with a big ratio difference a large p value (>0.05) with a trivial ratio difference Analyzing expression data

92. Inferential statistics Is it appropriate to set the significance level to p < 0.05? If you hypothesize that a specific gene is up-regulated, you can set the probability value to 0.05. You might measure the expression of 10,000 genes and hope that any of them are up- or down-regulated. But you can expect to see 5% (500 genes) regulated at the p < 0.05 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 < (0.05)/10,000 or p < 5 x 10 -6 The Bonferroni correction is generally considered to be too conservative.

93. Inferential statistics: 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 10,000 genes and a p value of 0.01, there are 100 expected false positives). You can adjust the false discovery rate. For example: FDR # regulated transcripts # false discoveries 0.1 100 10 0.05 45 3 0.01 20 1 Would you report 100 regulated transcripts of which 10 are likely to be false positives, or 20 transcripts of which one is likely to be a false positive?

95. log fold change (treated/untreated) p value (treated versus control) A volcano plot displays both p values and fold change

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

100. 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

101. What is a cluster? 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.

102. Data matrix (20 genes and 3 time points from Chu et al., 1998) Software: S-PLUS package genes samples (time points)

103. 3D plot (using S-PLUS software) t=0 t=0.5 t=2.0

104. Descriptive statistics: clustering Clustering algorithms offer useful visual descriptions of microarray data. 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 355

105. Distance Is Defined by a Metric Euclidean Pearson* Distance Metric: 6.0 1.4 +1.00 -0.05 D D

106. Distance is Defined by a Metric 4.2 1.4 -1.00 -0.90 Euclidean Pearson(r*-1) Distance Metric: D D

108. Agglomerative clustering a b c d e a,b 4 3 2 1 0 Adapted from Kaufman and Rousseeuw (1990)

109. a b c d e a,b d,e 4 3 2 1 0 Agglomerative clustering

110. a b c d e a,b d,e c,d,e 4 3 2 1 0 Agglomerative clustering

111. a b c d e a,b d,e c,d,e a,b,c,d,e 4 3 2 1 0 Agglomerative clustering … tree is constructed

112. Divisive clustering a,b,c,d,e 4 3 2 1 0

113. Divisive clustering c,d,e a,b,c,d,e 4 3 2 1 0

114. Divisive clustering d,e c,d,e a,b,c,d,e 4 3 2 1 0

115. Divisive clustering a,b d,e c,d,e a,b,c,d,e 4 3 2 1 0

116. Divisive clustering a b c d e a,b d,e c,d,e a,b,c,d,e 4 3 2 1 0 … tree is constructed

117. divisive agglomerative a b c d e a,b d,e c,d,e a,b,c,d,e 4 3 2 1 0 4 3 2 1 0 Adapted from Kaufman and Rousseeuw (1990)

120. 1 1 12 12 Agglomerative and divisive clustering sometimes give conflicting results, as shown here

122. Single Linkage Cluster-to-cluster distance is defined as the minimum distance between members of one cluster and members of the another cluster. Single linkage tends to create ‘elongated’ clusters with individual genes chained onto clusters. D AB = min ( d(u i , v j ) ) where u  A and v  B for all i = 1 to N A and j = 1 to N B (HCL-7) D AB

123. Average Linkage Cluster-to-cluster distance is defined as the average distance between all members of one cluster and all members of another cluster. Average linkage has a slight tendency to produce clusters of similar variance. D AB = 1/(N A N B )  ( d(u i , v j ) ) where u  A and v  B for all i = 1 to N A and j = 1 to N B (HCL-8) D AB

124. Complete Linkage Cluster-to-cluster distance is defined as the maximum distance between members of one cluster and members of the another cluster. Complete linkage tends to create clusters of similar size and variability. D AB = max ( d(u i , v j ) ) where u  A and v  B for all i = 1 to N A and j = 1 to N B (HCL-9) D AB

125. Comparison of Linkage Methods Single Average Complete

126. Two-way clustering of genes (y-axis) and cell lines (x-axis) (Alizadeh et al., 2000)

127. A B x 1 x 2 1 1 0.5 0.5 1.5 A’ B’ a 1 b 1 a’ 1 b’ 1 a’ 2 b 2 a 2 b’ 2    Euclidean distance Chord distance Angle distance

128. K-Means/Medians Clustering – 1 1. Specify number of clusters , e.g., 5. 2. Randomly assign genes to clusters. G1 G2 G3 G4 G5 G6 G7 G8 G9 G10 G11 G12 G13

129. K-Means/Medians Clustering – 2 3. Calculate mean/median expression profile of each cluster. 4. Shuffle genes among clusters such that each gene is now in the cluster whose mean expression profile (calculated in step 3) is the closest to that gene’s expression profile. 5. Repeat steps 3 and 4 until genes cannot be shuffled around any more, OR a user-specified number of iterations has been reached. k -means is most useful when the user has an a priori hypothesis about the number of clusters the genes should belong to. G1 G2 G3 G4 G5 G6 G7 G8 G9 G10 G11 G12 G13

131. An exploratory technique used to reduce the dimensionality of the data set to 2D 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). Principal components analysis (PCA)

132. 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

133. http://www.okstate.edu/artsci/botany/ordinate/PCA.htm

134. http://www.okstate.edu/artsci/botany/ordinate/PCA.htm

135. http://www.okstate.edu/artsci/botany/ordinate/PCA.htm

136. http://www.okstate.edu/artsci/botany/ordinate/PCA.htm

139. 1 12

140. [email_address] High-throughput methods beyond microarrays

143. [email_address] RNA-seq