Introduction to microarray technology Data analysis (prefiltering, normalization, gene identification)

Transcription

1 Computational Biology I Introduction to microarray technology Data analysis (prefiltering, normalization, gene identification)

2 Introduction to Microarray Technology H. Yang

3 Central Dogma DNA (genome sequencing) transcription mrna (RT-PCR,cDNA microarray) translation PROTEINS (2-D gel electrophoresis/ms/proteomic array) Activities/Functions/Malfunctions (diseases)

4 Why study Gene Expression? Human: 30,000 genes but 300,000 proteins Genetic engineering Gene-gene interactions No large-scale at proteomic level Large-scale at genomic level Deletion/ overexpression RNAi Drug DNA mrna Proteins Disease

5 cdna Microarray Compared to Conventional Methods Like

6 cdna Microarray not mrna Microarray mrna is very instable, while DNA is relatively much stable Reverse-transcription 5 cdna DNA 3 Transcription mrna 5 cdna Reverse transcription 3

7 cdna Microarray Technology Principle AATGTCCAGCAGCGT CAGGTCGTC GTCCAGCAG cdna of gene j Steps cdna j + cdna j DNA j 3 mrna j (on array) Total RNA 2 4 (on array) Sample. Extraction of total RNA 2. Isolation of mrna 3. RT-PCR & labeling 4. Hybridization 5. Wash (get ride of unbound dyes/material) 6. Scanning

8 Hybridization AATGTCCAGCAGCGTTTTT CAGGTCGTC

9 cdna Microarray Technology Making DNA microarray Put down denatured DNA of different genes (oligos: 20, bp or ORFs: bp) Gene Gene 2 The sequence for one gene should be unique & different from sequences of other genes

10 cdna Microarray Technology Design & synthesis of oligonucleotide

11 Putting Probes/Samples on a Chip Chip material Glass coated (polylysine/aldehyde/amino/epoxy) Membrane (coated nylon, plastic) Material spotted or grown on chips cdna (>500 bp) spotted Oligomers (50-80 bp) synthesized & spotted Oligomers (20-25 bp) grown Samples sample per chip (oligos/cdna membrane arrays) 2 samples co-hybridized on one chip (cdna glass arrays) using different dyes (Cy3 & Cy5)

12 2 Samples on a Chip Sample2 Sample

13 Chip Fabrication cdna microarray Affymetrix chip cdna library Public gene bank CAGGTCGTC A* (photolithography) G* C CA CAGGTCGTC Growth

14 Perfect Match and Mismatch On Chip In sample cdna i cdna j CAGGTCGTC GTCCAGCAG GTCCCGCAG cdna i + cdna i DNA i Perfect match cdna j + cdna i DNA ji Mismatch (in sample) (on array) (on array)

15 Temperature Effects on Binding 20 C 30 C 40 C 50 C 60 C All other spots are mismatches Perfect matches 85 C

16 DNA Microarray Technology Temperature effects on binding strength for perfect match & mismatch On Chip In sample Perfect match Mismatch CAGGTCGTC GTCCAGCAG GTCCCGCAG

17 DNA Microarray Technology Short sequence CAGGTCGTC GTGTC Long sequence TCCACAGGTCGTCGGCAG GTGTC Sensitivity (Specificity) The longer the probe, the stronger the binding, and thus the higher the intensity Selectivity (opposite of cross-hybridization) The shorter the probe, the weaker the binding, and thus the lower the cross-hybridization Secondary structure The longer the probe, the higher probability formation of secondary structure

18 Different Types of DNA Microarray cdna microarray Using >500 bp ORFs (2-3 replicates) High sensitivity (high signal), low selectivity (high cross-hybridization), secondary structure Oligo microarray Using bp oligomers (2-3 replicates) Moderate sensitivity, moderate selectivity Affymetrix chip Using bp oligomers (-20 pieces) Low sensitivity, high selectivity

19 Correction of Background & Cross-hybridization Background + cdna i + Chip Using space surrendering spots Cross-hybridization (non-specific binding) Using negative controls (cdna array) Using mismatch spots (Affymetrix) Corrected intensity x i = detected intensity background intensity non-specific binding

20 Comparative Genomics Gene expression e i --- expression intensity of gene i e j --- expression intensity of gene j Microarray measurement x i --- measured intensity of gene i x j --- measured intensity of gene j No stable standardization curve but ratio-wise there exists the following equations e x

21 Comparative Genomics Microarray Conventional e i e j = x i x j f i y i = = r e x i i i y i --- measured intensity of gene i on another array f i --- expression intensity of gene i on another array

22 Data analysis (prefiltering, normalization, gene identification) H. Yang

23 Prefiltering After subtraction of background & nonspecific binding, intensities of some spots < 0 or ~ 0, e.g. x i =0 & y i =60 ) x i =, 5, 0, 20, 50 (small number) y i /x i 60/, 60/5, 60/0, 60/20, 60/50 2) x i = 2 Std for x i < 2 Std background intensity + non-specific binding Std Log ratio r i Log mean intensity log x i y i

24 How to Make Sense of Microarray Data? x & y --- expression intensities of two samples gained from two different membranes/channels y What genes are expressed differently Normalization x

25 Comparative Genomics 0 Log ratio r Log mean intensity log xy Systematic shift (non-linear) Scattering (random error)

26 Errors Occurring at Pre-Analysis Steps Sample processing RNA isolation: Sample RNA mrna extraction: RNA mrna RT+labeling: mrna cdna+fluor dye Sample hybridization and microarray quality Variations in Microarray usage Microarray quality mrna amount used hybridization washing scanning array surface and printing quality spotted cdna dye bias

27 Processing/Measurement Errors System error (Sensor shift) Random error (Making differentiation in expression unclear) Microarray Data Analysis Steps Normalization Eliminate system error Identification of differentially expressed genes Look for expression beyond random error

28 Normalization Procedure Selection of a group of genes, e.g. using All genes, housekeeping genes, a special set of genes, non-differentially expressed genes, Application of a certain operator or metric Mean, medium, ratio mean, log ratio mean, linear regression, nonlinear regression,

29 Global Mean Normalization x i --- expression intensity of gene i on chip/channel y i --- expression intensity of gene i on chip/channel 2 Total No. of genes Normalization factor N N = i N N i y x i i

30 Housekeeping Gene LS Normalization y G29 G G43 G47 G3 G2 G3, G8, 34 G45 y = x Normalization factor x

31 0 LOWESS Normalization Locally weighted regression smoother n --- number of genes in the interval lr i --- logarithmic ratio of gene i lr i lr = log y i x i J =min Σ (lr-lr i ) 2 lr n i= lr = Σ N n lr i= i lr i Local log ratio mean regression smoother

32 Selection of Genes Used for Normalization Using only non-differentially expressed genes Exclude differentially expressed genes, since normalization is used for eliminate system errors and the system errors can be evaluated only with genes of the same expression. Nonlinearity exists Intensity-dependent approach (window-wise) Log ratio r Log mean intensity log xy

33 Identification of Non-differentially Expressed Genes Normalization identification of differentially expressed genes Non-differentially expressed genes + differentially expressed genes = all genes Identification of non-differentially expressed genes normalization Chick & Egg Identification of extremely differentially expressed genes using outlier detection methods Using all other genes excluding those extremely differentially genes for normalization

34 Normalization Metric System errors: x* y* (free of random errors) Normalization factor: * * ), ( y x y x = λ Random errors: x=x*+ε x & y=y*+ε y y x y x y x y x y x ε ε ε ε = =

35 Ratio 0 Modeling Noise-to-Signal Ratios Two samples two microarrays Two samples one microarray Ratio Mean intensity Mean intensity Mean y x y x = y * + ε y = x * + ε ε E x ε = E y x y ε x + ε ) ( 0 x y x = 0 * * ( + ε ) ε x y = 0 y

36 Proposed Normalization Method Segmentation (S) of the expression intensity range into a number of intensity intervals Mean log normalization factor for each interval ( ) + = y x E y x E y x E y x ε ε λ log log ), ( log Using the Nearest Neighbor genes (thus: SNN) for determination of the means in each interval Array measurements Noise-to-signal ratios Mean

37 Results - Normalization Before & After Normalization Log ratio Log ratio samples array Log mean intensity Log mean intensity Log ratio Log ratio samples 2 arrays Log mean intensity Log mean intensity

38 Results - Normalization 0 Lowess Ratio (x/y) Mean intensity ( xy) Our approach

39 Normalization Error Freq uency Normalized log ratio Original log ratio Log of expression ratio or normalized ratio

40 Identification of Differentially Expressed Genes 2 or 3-fold change used as cutoff (3-fold = ln(ratio 3)=.) Log ratio Log mean intensity Statistical method for fold changes (no consideration of intensity) 3-fold Frequency Log normalized ratio

41 Intensity-dependent Approach 3 2 Log ratio Log mean intensity Frequency Log ratio

42 Percentile Method for Gene Identification 3 2 Log ratio Log mean intensity Frequency Log ratio

43 Identification of Differentially Expressed Genes What detected are two things added together r i 2.7 r i To identify differentially expressed genes, we need to separate differential expression from random errors using differential expression random errors Identical microarray experiment

44 Identical Microarray Experiment One sample Sample Sample 2 No differential expression because of the same sample. Difference of log r i from 0 should be caused due to random errors only. Distribution of log r i should be used to estimate the significance of ratios

45 New Gene Identification Method Standardized distributions of normalized ratio are similar for different intensity intervals and arrays. Percentile method confidence intervals (multiplier). Mask boundaries: ± multiplier Std. 0.3 Probability density multiplier 2.5% 2.5% Standardized log of normalized ratio

46 Identification of Differentially Expressed Genes 3 2 HM A pair of plastic arrays Log ratio SNN-LERSD MF&T ERPD Log mean intensity

47 Validation of Gene Identification Methods Methodology Genetically modified strains vs Wild Type Using genes deleted or overexpressed Quantitative RT-PCR as references Spiking of specific clones into one sample not in another sample Using those specific genes