Canadian Bioinforma2cs Workshops

Transcription

1 Canadian Bioinforma2cs Workshops Module #: Title of Module 2 1

2 Introduction to Microarrays & R Paul Boutros Morning Overview 09:00-11:00 Microarray Background Microarray Pre- Processing Basics 11:30-12:30 Guided Analysis: Data Loading & Pre Processing 2

3 Let s start off with a ques7on What do expression microarrays actually measure? Session Overview What are microarrays? What are microarrays used for? Molecular Aspects Biological Aspects Downstream Analyses How is microarray data analyzed? Workflow overview 3

4 What is a Microarray? A DNA microarray is a mul2plex technology consis2ng of thousands of oligonucleo2de spots, each containing picomoles of a specific DNA sequence. Used to quan2tate mrna or DNA Many applica2ons: mrna or DNA levels SNP iden2fica2on ChIP- on- Chip Hypotheses Microarrays are usually hypothesis- genera2ng: They highlight specific genes or features that are par2cularly interes2ng for follow- up experiments There are many interes2ng excep2ons Biomarkers Pathway analyses This does not reduce the importance of experimental design the low sta2s2cal power of array studies make good design even more important and very challenging 4

5 Input Samples The nature of the sample is critical: * Unfrozen vs. Frozen vs. FFPE * Total RNA vs. poly-a RNA vs. other subsets Microarray Basics Imagine a one- spot microarray Target Target DNA is labeled and hybridized and washed. Finally, scan the chip. Feature Chip Probe 5

6 These Are SpoDed Arrays Robotically printed onto a series of glass slides using a robot with needle-heads. Product a characteristic gridding pattern and almost always use two samples simultaneously (two-colour). 6

7 Other Types of Arrays Inkjet Arrays Photolithographically generated arrays Bead arrays Protein/cell/lipid- arrays More niche applica2ons Not discussed here InkJet Arrays In 1999, HP spun off its life-science and measurement division into Agilent Technologies. The new company wanted to determine if printer technology could be harnessed to generate microarrays. 7

8 Inkjet Array Manufacture Involves Sequen7al Nucleo7de Addi7on Photolithographic Arrays Produced by the techniques for the produc2on of transistors. Mostly pioneered by the company Affymetrix, although other suppliers exist (e.g. Nimblegen) We will be working with Affymetrix data later, so we will walk through the pladorm in significant detail 8

9 The Glass Matrix Silination Addition of Linker molecule Photolithographic Synthesis Photolithographic mask 9

10 Deprotection Nucleotide Addition 10

11 Nucleotide Addition Nucleotide Addition 11

12 Capping Agents Final Chip Wafer Feature Chip 12

13 RNA Wash 13

14 RNA Wash An Affymetrix Microarray 14

15 We Can Change Those Mappings! Hybridized Chip Self- Assembling Bead- Arrays Produced by Illumina 3 μm silicon beads, randomly placed coated with ~10 5 iden2cal 25bp probes probes have iden2fying barcode (address) sequences Labeled cdna bead address probe 15

16 Comparing Array PlaOorms Platform Price Oligos Spotted cdna $ variable Data Quality + Bioinformatics Research +++ Affymetrix $$$ 25 bp Inkjet $$ ~70 bp Bead Arrays $$ ~25 bp ++ + I do not endorse specific platforms they all have their strengths and weaknesses Session Overview What are microarrays? What are microarrays used for? Molecular Aspects Biological Aspects Downstream Analyses How is microarray data analyzed? Workflow overview 16

17 What Are Microarrays Used For? Molecular DNA RNA Other DNA sequence (SNPs) DNA copy- number DNA capture (exome, ChIP) Tag quan2ta2on (gene2c screening) mrna abundances Splicing (quan2tate different isoforms) mrna degrada2on rates (half- life) mrna transla2on rates RNA capture (RIP) Protein arrays Cell based arrays Lipid arrays What Are Microarrays Used For? Biological RNA mrna abundances Splicing (quan2tate different isoforms) mrna degrada2on rates (half- life) mrna transla2on rates RNA capture (RIP) * Candidate Gene Identification * Pathway Analysis * Model Characterization * Classifiers/Predictive Models * Drug-Analysis (Dose/Time/Class) * Integration Analysis 17

18 What are microarrays? Session Overview What are microarrays used for? Molecular Aspects Biological Aspects Downstream Analyses (upcoming sessions: pathways & clinical integra2on) How is microarray data analyzed? Workflow overview Each Spot is a Probe Quantitation Spot Cy3 Cy5 Background A) Remove Noise Spot Quality Inter-array Intra-Array Spot List Significance Testing? Clustering Integration B) Extract Data 18

19 Step #1: Image Quan7ta7on Why? How? Difficulty? Research? Module 1 Quan2ta2ve vs. Qualita2ve Image Segmenta2on Image Segmenta7on 101: Find Grids 1. Find Grids 2. Find Spots 3. Spot Outline Module 1 19

20 Image Segmenta7on 101: Find Spots Key Step: Integrate Signal Across Array Image Segmenta7on 101: Challenges Problems: Stray Signal Missing Spots Gross Deformities Manual Validation 20

21 Research? Surprisingly, not much inves2ga2on This is probably a source of error in all studies Manual checking of spot- detec2on remains the norm Problema2c as studies & arrays get larger Quantitation Spot Cy3 Cy5 Background Spot Quality Inter-array Intra-Array Significance Testing Spot List Clustering Integration? 21

22 Step #2: Background Correc7on Why? Remove Stray Signal How? Model- based Difficulty? ++++ Research? ++ Spot Segmenta7on Signal??? Background 22

23 So what do we get? Background Intensity: BG Foreground Intensity: FG Isn t it simple? Signal = FG - BG NO! If BG > FG Then -ve Signal 0.1-2% of spots Why Might This happen? In 2001 two papers showed that empty spots have less signal than background Unbound spots correspond to low-expression genes Background Intensity: BG Foreground Intensity: FG Thus unbound spots are particularly prone to problems 23

24 So What to Do? Heavy- duty mathema2cal tools employed Three major models developed: Edwards log- linear Smyth normexp Kooperberg Bayesian The math is extremely advanced, so we ll skip that for now. Let s summarize the methods instead. Comparison Method Speed Accuracy Edwards NormExp Kooperberg Fast Slow Very Slow Good Better Best No strong criteria for selecting between these algorithms. 24

25 Quantitation Spot Cy3 Cy5 Background Spot Quality Inter-array Intra-Array Significance Testing Spot List Clustering Integration? Step 3: Spot Quality Why? How? Difficulty? Research? + Iden2fy artefacts Unknown 25

26 Spot- Weigh7ng A perfect spot is used normally in analysis Weight = 1 A poor spot is given less considera2on 0 < Weight < 1 Problem: How the heck do we calculate weights? A Few Approaches Mean- Median Correla2on Composite q- metrics è improve homotypic signal:noise But both fail some2mes, seemingly randomly. Do we really need this? 26

27 All from one good-quality array! But I use Affymetrix! (Or Agilent) (Or Nimblegen) (Or Other Commercial Supplier) 27

28 Okay, Let s See Some Affy Data 28

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30 Those Three Were From A Spike- In Experiment Done by Affymetrix Themselves! 30

31 Module 1 Module 1 31

32 Spot Quality is An Issue, Regardless of PlaOorm Manual Flagging? Two studies show error rates of 5-20% Spot-Quality is a huge, unsolved problem. Most investigators ignore it. More bioinformaticians struggle with it. Then we ignore it too. 32

33 Quantitation Spot Cy3 Cy5 Background Spot Quality Inter-array Intra-Array Significance Testing Spot List Clustering Integration? Step 4: Intra- Array Normaliza7on Why? Balance channels Remove spa2al ar2facts How? Mul2ple robust algorithms Difficulty? ++ Research?

34 Within- Array Normaliza7on 1. Spa2al gradients 2. Channel- balancing 3. Intensity bias Are red and green equal in our starting sample? We Can Handle This! Spa2al Effects: Gaussian Spa2al Smoothing Intensity Effects: Loess Smoothing Combina2on Effects: Robust Splines All methods well-established 34

35 Quantitation Spot Cy3 Cy5 Background Spot Quality Inter-array Intra-Array Significance Testing Spot List Clustering Integration? Step 5: Inter- Array Normaliza7on Why? How? Difficulty? + Research? Balance arrays Mul2ple robust algorithms 35

36 Balancing Arrays Problem: Pipene error can lead to differen2al loading of sample between arrays Solu2on: Scale arrays Extremely easy to handle Scaling Has a Major Effect Intensity Before After p(i) 36

37 Quantitation Spot Cy3 Cy5 Background Spot Quality Inter-array Intra-Array Significance Testing Spot List Clustering Integration? We are on a Coffee Break & Networking Session 37

38 What Is BioConductor? Bioconductor is an open source, open development so4ware project to provide tools for the analysis and comprehension of high- throughput genomic data. - BioConductor website The vast majority of our analyses will use BioConductor code, but there are clearly non-bioconductor approaches. I ve outlined the general workflow. Each technology and applica7on has its own unique characteris7cs to consider. 38

39 Let s Define an Affymetrix- Specific Workflow Quantitation is done Spot Cy3 Cy5 according to Affymetrix Quantitation Background defaults with minimal user intervention. One-Channel array Spot Quality Significance Testing Inter-array Single-Channel array, so one simultaneous Intra-Array normalization procedure Typically ignored Spot List Clustering Integration? 39

40 Let s Collapse This a Bit And Re- Phrase Things.CEL Files Background Normalization ProbeSet Annotation Clustering Spot List Statistics Integration? 40

41 Arrays Can Become Outdated Gene defini2ons change The reference genome sequence gets finished Novel splice variants are found Errors are made in the ini2al design and remain present in all arrays made The Mask Produc7on Makes Affymetrix Designs Expensive To Change Photolithographic mask 41

42 Let s go Back to Pre- Processing What exactly is pre-processing (aka normalization)? Why do we do it? Sources of Technical Noise Where does technical noise come from? 42

43 More Sources of Technical Noise Any step in the experimental pipeline can introduce ar7factual noise Array design Array manufacturing Sample quality Sample iden2ty à sequence effects? Sample processing Hybridiza2on condi2ons à ozone? Scanner seongs Pre-Processing tries to remove these systematic effects 43

44 Always try to balance experimental groups. Important Note Pre-processing is never a substitute for good experimental design. This is not a course on statistical design, but a few basic principles should be mentioned. Biological replicates are preferable to technical replicates. If processing samples identically is not possible, include controls for processing-effects. Introducing Two Major Affymetrix Pre- Processing Methods The two most commonly used methods are: RMA = Robust Mul2- array MAS5 = Microarray Analysis Suite version 5 MAS5 has strengths & weaknesses Sacrifices precision for accuracy Can easily be used in clinical seongs RMA has strengths & weaknesses Sacrifices accuracy for precision Challenging to integrate mul2ple studies Reduces variance (cri2cal for small- n studies) RMA is bener accepted by journals and reviewers 44

45 We ll talk about the maths behind each method this agernoon, but first let s actually apply them to some real data! We are on a Coffee Break & Networking Session 45