Introduction to Microarray Gene Expression.ppt

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1、Introduction to Microarray Gene Expression,Shyamal D. Peddada Biostatistics Branch National Inst. Environmental Health Sciences (NIH) Research Triangle Park, NC,Outline of the four talks,A general overview of microarray dataSome important terminology and background Various platforms Sources of varia

2、tion Normalization of dataAnalysis of gene expression data - Nominal explanatory variablesTwo types of explanatory variables Scientific questions of interest A brief discussion on false discovery rate (FDR) analysis Some existing methods of analysis.,Outline of the four talks,Analysis of ordered gen

3、e expression dataCommon experimental designs Some existing statistical methods An example Demonstration of ORIOGEN Some open research problemsAnalysis of data from cell-cycle experimentsSome background on cell-cycle experiments Modeling the data Data from multiple experiments Some open research prob

4、lem,Talk 1: An overview of microarray data,To perform statistical analysis of any given data,It is important to understand all sources of (i) bias, (ii) variability.Some basic understanding of the underlying technology! Understand the sampling/experimental design,Some Important Terminology and Backg

5、round,Central Dogma of Molecular Biology,Some background terminology: DNA and RNA,DNA (Deoxyribonucleic acid) - Contains genetic code or instructions for the development and function living organisms. It is double stranded.Four Nucleotides (building blocks of DNA)Adenine (A), Guanine (G), Thymine (T

6、), Cytosine (C)Base pairs: (A, T) (G, C)E.g. 5 -AAATGCAT-33 -TTTACGTA-5,Some background terminology: DNA and RNA,RNA (Ribonucleic acid) - transcribed (or copied) from DNA. It is single stranded. (Complimentary copy of one of the strands of DNA)RNA polymerase - An enzyme that helps in the transcripti

7、on of DNA to form RNA.Four Nucleotides (building blocks of DNA)Adenine (A), Guanine (G), Uracil (U), Cytosine (C) Base pairs: (A, U) (G, C),Some background terminology: Types of RNA,Types of RNA - (transfer) tRNA, (ribosomal) rRNA, etc. mRNA - messenger RNA. Carries information from DNA to ribosomes

8、 where protein synthesis takes place (less stable than DNA).,Some background terminology: Oligos,Oligonucleotide - a short segment of DNA consisting of a few base pairs. In short it is commonly called “Oligo”.“mer” - unit of measurement for an Oligo. It is the number of base pairs. So 30 base pair O

9、ligo would be 30-mer long.,Some background terminology: Probes,cDNA - complimentary DNA. DNA sequence that is complimentary to the given mRNA.Obtained using an enzyme called reverse transcriptase. Probes - a short segment of DNA (about 100-mer or longer) used to detect DNA or RNA that compliments th

10、e sequence present in the probe.,Some background terminology: “Blots” - Origins of Microarrays,Southern blot (Edwin Southern, 1975 J. Molec. Biol.)A method used to identify the presence of a DNA sequence in a sample of DNA.Western blot (immunoblot)to identify a specific protein from a tissue extract

11、.,Some background terminology,Southwestern blotto identify and characterize DNA-binding proteins.Northern blotA method used to study the gene expression from a sample of mRNA.,Microarrays ,Northern blot Vs Microarray,What is a Microarray?,Sequences from thousands of different genes are immobilized,

12、or attached, at fixed locations. Spotted, or actually synthesized directly onto the support.,Microarray Technology,Two color dye array (Spotted array)Spotted cDNA microarrays Spotted oligo microarraysSingle dye arrayIn situ oligo microarrays,Microarray Technology,Spotted Microarrays,Spotted DNA Micr

13、oarray,Slides carrying spots of target DNA are hybridized to fluorescently labeled cDNA from experimental and control cells and the arrays are imaged at two or more wavelengths Expression profiling involves the hybridization of fluorescently labeled cDNA, prepared from cellular mRNA, to microarrays

14、carrying thousands of unique sequences.,Spotted DNA Microarray,Spotted DNA array is typically “home made” so you need to think about:cDNA or Oligo Location of the Oligo in a given gene Oligo length - number of bp?,Spotted DNA Microarray,Gene expression:Y 0; gene is over expressed in red-labeled samp

15、le compared to green labeled sample,Single Dye Microarrays,Major Commercial Platforms,More than 50 companies are currently offering various DNA microarray platforms, reagents and software Affymetrix dominated the marker for many years,*Agilent has one and two-color microarray platform,Affymetrix Gen

16、eChip,Each gene is represented by 11 to 20 oligos of 25-mersProbe: An oligo of 25-merProbe Pair: a PM and MM pairPerfect match (PM): A 25-mer complementary to a reference sequence of interest (part of the gene)Mismatch (MM): same as PM with a single base change for the middle (13th) base (G C, A T)P

17、robe set: a collection of probe-pairs (11 to 20) related to a fraction of gene,Affymetrix call for the presence of a signal,Affymetrix detection algorithm uses probe pair intensities to obtain detection p-value Using this p-value they decide whether the signal is “ present”, “marginal” or “absent”,A

18、ffy call,Detection of p-valueCalculate Kendalls tau T for each probe pairT = (PM-MM) / (PM+MM)Determine the statistical significance of the gene by computing the p-value.,Affy call,Ref: Affymetrix Technical Manual,Affymetrix Vs Illumina,Ref: Pan Du & Simon Lin,Which Platform to Choose?,Every platfor

19、m has its unique featureChoose platform based onNature of the study Amount of available RNA CostPlatform comparison in MAQC study,MAQC Project,Objective: To generate a set of quality control tools for microarray research community137 participants representing 51 organizationsGene expression from two

20、 distinct RNA samples (total 4 samples)Sample A = Universal Human Reference RNA(UHRR)100% Sample B = Human Brain Reference RNA(HBRR) 100% Sample C = 75% UHRR + 25% HBRR Sample D = 25% UHRR + 75% HBRR,Microarray Data Analysis,Why Normalize Data?,To “calibrate”/adjust data so as to reduce or eliminate

21、 the effects arising from variation in technology and other sources rather than due to true biological differences between test groups.,Sources of bias/variation,Tissue or cell linesmRNAIt can degrade over time - so there is a potential batch effect if portions of experiment are performed at differe

22、nt times Purity and quantityDye color effect (spotted arrays)Variation due to technology - is substantially reduced with improved technologyEtc.,A useful graphical representation of data,Data matrix:Let,A useful graphical representation of data,Let its spectral decomposition be given bywhere,A usefu

23、l graphical representation of data,ThenPlot,Common Normalization Methods,Internal Control NormalizationGlobal NormalizationLinear Normalization (Spotted arrays)Non-linear Normalization Method (Spotted arrays) - LOWESS curve.ANOVA COMBAT (for batch effect),Internal control normalization (Housekeeping

24、 gene(s),Expression of each gene is measured relative to the average of house keeping genes.Basic assumption: Expression of housekeeping genes does not change.Disadvantage: House keeping genes may be highly expressed sometimes. Unexpected regulation of house keeping gene(s) leads to misinterpretatio

25、n,Global Normalization,Basic assumptionMean/Median expression ratio of all monitored mRNAs is constant across a chip.Regression ofIn simple terms the log ratios are corrected by a common “mean” or “median”This method can also be applied to single Dye data,Linear Normalization (for spotted arrays),Ba

26、sic assumptionMean/Median expression ratio of all monitored mRNAs depends upon the average intensityRegression of,Non-Linear Normalization (for spotted arrays),Basic assumptionMean/Median expression ratio of all monitored mRNAs depends upon the average intensityRegression ofWhere is estimated by the

27、 robust scatter plot smoother LOWESS (Locally WEighted Scatterplot Smoothing),Analysis of Variance (ANOVA),Standard Analysis of Variance modelResponse variable - Gene expression Explanatory variables:Dye colorBatchOther potential effects?Advantage: Statistically significant genes can be identified w

28、hile controlling for the various experimental conditions/factors.,Some important experimental designs,Pooled Samples versus Separate samplesSometimes there may not be sufficient biological sample/specimen from a given animal. In such cases biological samples are pooled from several identical animals

29、 to form a sample.,An example of a pooling design (for each treatment group),Subjects Pool Observations (Microarray chips),The pooling design,Subjects Pool Observations (Microarray chips)9 3 6(3 per pool)More generally: n p m(r=n/p per pool),The standard design,Subjects # Pool Observations (Microarr

30、ay chips)9 9 9(r=1) More generally: n p=n m=n(r=1),Some issues,What are the underlying parameters? Effect of pooling on power. The basic assumption. Validity of the assumption.,Parameters,Total variation in the expression of a gene can be decomposed in to:Biological variation Technical variationBiol

31、ogical samples (n) Number of pools (p) Biological samples per pool (r=n/p) Observed number of samples (e.g. microarrays) (m),Some comments about pooling,Variance of the estimated mean expression of a gene depends on:number of pools (p) number of bio samples per pool (r) number of arrays (m) biologic

32、al variation Technical variation.Pooling works well when the biological variation in the gene expression is substantially larger than the technical variation.,Power comparisons,# Bio #Micro Pool size Power5/group 5/group 1 (Standard design) 0.81 6/group 6/group 1 (Standard design) 0.956/group 3/grou

33、p 2 (i.e 3 pools/group) 0.30 8/group 4/group 2 (i.e. 4 pools/group) 0.80 10/group 5/group 2 (i.e. 5 pools/group) 0.98Zhang and Gant (2005),Power comparisons,Conditions of the simulation study:Biological variation is 4 times the technical variation.False positive rate is 0.001. Detect 2-fold expressi

34、on.Data are normally distributed.,A fundamental assumption,Biological averaging:Suppose an experiment consists of pooling “r” samples. Then the expression of a gene in the pooled sample is assumed to be the average of the genes expression in the “r” samples.This assumption need not be true especiall

35、y if the expression values are transformed non-linearly.,Some important experimental designs,Reference designs (Spotted array)Each treatment sample is hybridized against a common reference control. Loop designs (Spotted array)Suppose we have a control and three experimental groups A, B and C. Then h

36、ybridize Control and A, A with B, B with C and C with A.,Data Analysis - Preliminaries,Normalization Transformation of data (usual methods)Perhaps first fit ANOVA and plot the residualsLog transformation Square root More generally, Box-Cox family of transformationsIdentify potential outliers in the data (again, perhaps use the residuals),Data Analysis,Method of Analysis depends upon the scientific question of interest.In the next three lectures we describe several general methods and illustrate some using real data!,