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Microarray Yuki Juan NTUST May 26, 2003. Content  Biology background of microarray  Design of microarray  The workflow of microarray  Image analysis.

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Presentation on theme: "Microarray Yuki Juan NTUST May 26, 2003. Content  Biology background of microarray  Design of microarray  The workflow of microarray  Image analysis."— Presentation transcript:

1 Microarray Yuki Juan NTUST May 26, 2003

2 Content  Biology background of microarray  Design of microarray  The workflow of microarray  Image analysis of microarray  Data analysis of microarray  Discussion

3 The Biology Background of Microarray  The central dogma of life forms  DNA  RNA  Monitoring the expression of genes

4 Central Dogma  DNA Replication --ACGCGA-- --TGCGCT--  RNA Transcription --UGCGCU--  Protein Translation --CYSALA--

5 DNA RNAProtein replication transcriptiontranslation DNA

6  The double helix stable  Nucleotide A, T, G, C  Base pair A – T G – C  Oligonucleotide short DNA (tens of nucleotides, or bps) (

7 DNA Strand  DNA has canonical orientation read from 5 ’ to 3 ’ antiparallel: one strand has direction opposite to its complement ’ s 5’ … TACTGAA … 3’ 3’ … ATGACTT … 5’

8 Hydrogen Bond Makes DNA Binding Specifically 5’ 3’ 5’ Hydrogen bond

9 Hydrogen Bond Makes DNA Binding Specifically  The force between base pair is hydrogen bond, This force let A-T(U), C-G can specifically match together.

10 DNARNA Protein replication transcriptiontranslation RNA

11  Types messenger RNA ribosomal RNA (rRNA) transfer RNA (tRNA) Gene is expressed by transcribing DNA into single-stranded mRNA

12 RNA (Detailed) (

13 DNARNA Protein replication transcriptiontranslation Reverse Transcription By reverse transcriptase, we can convert RNA into cDNA.

14 The Southern Blot  Basic DNA detection technique that has been used for over 30 years, known as Southern blots: A “ known ” strand of DNA is deposited on a solid support (i.e. nitocellulose paper) An “ unknown ” mixed bag of DNA is labelled (radioactive or flourescent) “ Unknown ” DNA solution allowed to mix with known DNA (attached to nitro paper), then excess solution washed off If a copy of “ known ” DNA occurs in “ unknown ” sample, it will stick (hybridize), and labeled DNA will be detected on photographic film

15 mRNA Represent Gene Function  When measure the level of a mRNA, we are monitoring the activity of a gene.  Thus, if we can understand all the level of mRNAs, we can study the expression of whole genome.  Microarray takes the advantage of getting over of blotting data in a single experiment, which makes monitoring the genome activity possible.

16 Content  Biology background of microarray  Design of microarray  The workflow of microarray  Image analysis of microarray  Data analysis of microarray  Discussion

17 Design of Microarray  Microarray in different context  The idea of microarray  Main type of array chips

18 mRNA Levels Compared in Many Different Contexts Different tissues, same organism (brain v. liver) Same tissue, same organism (tumor v. non- tumor) Same tissue, different organisms (wt v. mutant) Time course experiments (development) Other special designs (e.g. to detect spatial patterns).

19 Cell ACell B Hybridizaton to chip Labeled cDNA from geneX Spot of geneX with complementary sequence of colored cDNA This spot shows red color after scanning. Idea of Microarray

20 Over 10,000 Hybridization Could Be Down at One Time

21 Several Types of Arrays  Spotted DNA arrays Developed by Pat Brown ’ s lab at Stanford PCR products of full-length genes (>100nt)  Affymetrix gene chips Photolithography technology from computer industry allows building many 25-mers  Ink-jet microarrays from Agilent mers “ printed directly on glass slides Flexible, rapid, but expensive

22 Array Fabrication Spotting Use PCR to amplify DNA Robotic "pen" deposits DNA at defined coordinates approximately 1-10 ng per spot Experimentation with oligos (40, 70 bp)

23 This machine can make 48 microarrays simultaneously.

24 Array Fabrication Photolithography Light activated synthesis synthesize oligonucleotides on glass slides 10 7 copies per oligo in 24 x 24 um square Use 20 pairs of different 25-mers per gene Perfect match and mismatch

25 Array Fabrication Photolithography

26 Affymetrix Microarrays 50um 1.28cm ~10 7 oligonucleotides, half perfectly match mRNA (PM), half have one mismatch (MM) Raw gene expression is intensity difference: PM - MM Raw image

27 Agilent cDNA microarray and oligonucelotides microarray  Agilent delivering printed 60-mer microarrays in addition to 25-mer formats.  The inkjet process uses standard phosphoramidite chemistry to deliver extremely small volumes (picoliters) of the chemicals to be spotted.

28 Content  Biology background of microarray  Design of microarray  The workflow of microarray  Image analysis of microarray  Data analysis of microarray

29 The Workflow of Microarray Array Hybridized Array Hybridization Scanning Plate Array Fabrication Plate Preparation RNA extraction Labeled cDNA cDNA synthesis and labeled sample

30 cDNA Synthesis And Directly Labeling

31 Cy3 and Cy5 cDNA Hybridization On To The Chip 1.Loading from the corner of the cover slip It is time consuming and easily producing bubbles. 2. Loading sample at the center of array then put the slip smoothly Faster, and have lower chance of bubble producing then the last one. 3. Loading sample at the side of the array then put the slip on. Solution would attach to the slip right after the slip contact with it, and would diffuse with the movement of slip when we slowly move down Sample loading e.g. treatment / control normal / tumor tissue Sample loading

32 Scan Green: down regulate Red: up regulate Yellow: equal level

33 Content  Biology background of microarray  Design of microarray  The workflow of microarray  Image analysis of microarray  Data analysis of microarray  Discussion

34 Image analysis  To find a spot  Convert feature into numeric data  Image normalization

35 The Algorithms 1. Find spots: Finds the location of each spot on the microarray. 2. Cookie cutter algorithm: (1).Suppose the distribution of pixels vs intensity is Gaussian curve (2).Using SD or IQR to identify the feature and background of each spot (3).Calculates statistics for the pixel population

36 Interquartile Range(IQR) 50 % 75 % 25 % K=IQR/21.42 IQR D Boundary for rejection IQR

37 Feature or cookie Exclusion zone Local background D

38 Data Quality  Irregular size or shape  Irregular placement  Low intensity  Saturation  Spot variance  Background variance indistinguishablesaturated bad print artifactmiss alignment

39 Green intensity Green background Green b.g.-corrected Red intensity Red b.g. Red b.g.-corrected (R. b.g.-c)/(G. b.g.- c) Systematic name Gene function Convert Feature Into Numeric Value

40 Data Normalization  Normalize data to correct for variances Dye bias Location bias Intensity bias Pin bias Slide bias  Control vs. non-control spots

41 Data Normalization Calibrated, red and green equally detected Uncalibrated, red light under detected

42 Data Normalization  Assumptions Overall mean average ratio should be 1  Most genes are not differentially expressed Total intensity of dyes are equivalent

43 Intensity Dependent Normalization

44 After Normalization

45 Additional Normalization  Pin dependent Similar to intensity dependent fit. Compute individual lowess fits for each pin group  Within slide normalization After pin dependent normalization, log ratios for each pin are centered around 0 Scale variance for each pin  Uses MAD (median absolute deviation)

46 Additional Normalization  Dye swap Combine relative expression levels without explicit normalization Compute lowess fit for log 2 (RR ’ /GG ’ )/2 vs. log 2 (A + A ’ )/2 Normalized ratio is log 2 (R/G) - c(A) where c(A) is the lowess prediction

47 Content  Biology background of microarray  Design of microarray  The workflow of microarray  Image analysis of microarray  Data analysis of microarray  Discussion

48 Data analysis  Data filtering  Fold change analysis  Classification  Clustering  Future direction

49 Microarray Data Classification Prediction: Gene Value D26528_at 193 D26561_cds1_at -70 D26561_cds2_at 144 D26561_cds3_at 33 D26579_at 318 D26598_at 1764 D26599_at 1537 D26600_at 1204 D28114_at 707 Data Mining and analysis New sample Microarray chipsImages scanned by laser Datasets

50 The Threshold of Spots  Filtering - remove genes with insufficient variation Remove insufficient spot: saturated, None uniform, too high background … Remove extreme signal: e.g. MaxVal - MinVal < 500 and MaxVal/MinVal < 5 Statistical filtering (e.g. p-value<0.01) biological reasons feature reduction for algorithmic

51 Microarray Data Analysis Types  Different gene expression Fold change analysis  Classification (Supervised) identify disease predict outcome / select best treatment  Clustering (Unsupervised) find new biological classes / refine existing ones exploration  …

52 Differential Gene Expression  n-fold change n typically >= 2 May hold no biological relevance Often too restrictive  2 expression Calculate standard deviation  Genes with expression more than 2 away are differentially expressed

53 Scatter Plot Fold Changes-Scatter Plot 21

54 Fold Changes Table 23

55 Classification: Multi-Class Similar Approach:  select top genes most correlated to each class  select best subset using cross-validation  build a single model separating all classes  Advanced: build separate model for each class vs. rest choose model making the strongest prediction

56 Popular Classification Methods  Decision Trees/Rules find smallest gene sets, but also false positives  Neural Nets - work well if number of genes is reduced  SVM good accuracy, does its own gene selection, hard to understand  K-nearest neighbor - robust for small number genes  Bayesian nets - simple, robust

57 Multi-class Data Example  Brain data, Pomeroy et al 2002, Nature (415), Jan examples, about 7,000 genes, 5 classes  Selected top 100 genes most correlated to each class  Selected best subset by testing 1,2, …, 20 genes subsets, leave-one- out x-validation for each

58 Classification – Other Applications  Combining clinical and genetic data  Outcome / Treatment prediction Age, Sex, stage of disease, are useful e.g. if Data from Male, not Ovarian cancer

59 Clustering Goals  Find natural classes in the data  Identify new classes / gene correlations  Refine existing taxonomies  Support biological analysis / discovery  Different Methods Hierarchical clustering, SOM's, etc

60 SOM clustering  SOM - self organizing maps  Preprocessing filter away genes with insufficient biological variation normalize gene expression (across samples) to mean 0, st. dev 1, for each gene separately.  Run SOM for many iterations  Plot the results

61 SOM & K Mean By GeneSpring 27

62 Hierarchical Clustering  The most popular hierarchical clustering method used in microarray data analysis is the so called agglomerative method works with the data in a bottom-up manner.  Initially, each data point forms a cluster and the algorithm works through the cluster sets by repeatedly merging the two which are the most similar or have the shortest distance. algorithm involves the computation of the distance or similarity matrix  O(N^2) complexity and thus is not very efficient.

63 Hierarchical clustering

64 Future directions  Algorithms optimized for small samples (the no. of samples will remain small for many tasks)  Integration with other data biological networks medical text protein data  cost-sensitive classification algorithms error cost depends on outcome (don ’ t want to miss treatable cancer), treatment side effects, etc.

65 Integrate biological knowledge when analyzing microarray data (from Cheng Li, Harvard SPH) Right picture: Gene Ontology: tool for the unification of biology, Nature Genetics, 25, p25

66 Content  Biology background of microarray  Design of microarray  The workflow of microarray  Image analysis of microarray  Data analysis of microarray  Discussion

67 Microarray Potential Applications  Biological discovery new and better molecular diagnostics new molecular targets for therapy finding and refining biological pathways Mutation and polymorphism detection  Recent examples molecular diagnosis of leukemia, breast cancer,... appropriate treatment for genetic signature potential new drug targets

68 Microarray Limitations Cross-hybridization of sequences with high identity Chip to chip variation True measure of abundance? Does mRNA levels reflect protein levels? Generally, do not “ prove ” new biology - simply suggest genes involved in a process, a hypothesis that will require traditional experimental verification. What fold change has biological relevance? Need cloned EST or some sequence knowledge -- rare messages may be undetected Expensive!! Not every lab can afford experiment repeat. The real limitation is Bioinformatics

69 Additional Information  Review papers on microarray Genomics, gene expression and DNA arrays (Nature, June 2000) Genomics, gene expression and DNA arrays (Nature, June 2000) Microarray - technology review (Natural Cell Biology, Aug. 2001) Microarray - technology review (Natural Cell Biology, Aug. 2001) Magic of Microarray (Scientific American, Feb. 2002) Magic of Microarray (Scientific American, Feb. 2002)  Molecular biology tutorial

70 Biological data retrieval systems: Entrez 1. A retrieval system for searching a number of inter-connected databases at the NCBI. It provides access to: PubMed: The biomedical literature (Medline) Genbank: Nucleotide sequence database Protein sequence database Structure: three-dimensional macromolecular structures Genome: complete genome assemblies PopSet: population study data sets OMIM: Online Mendelian Inheritance in Man Taxonomy: organisms in GenBank Books: online books ProbeSet: gene expression and microarray datasets 3D Domains: domains from Entrez Structure UniSTS: markers and mapping data SNP: single nucleotide polymorphisms CDD: conserved domains 2. Entrez allows users to perform various searches.

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