Analysis of Affymetrix and - PowerPoint Presentation

1. Analysis of Affymetrix and Illumina Array DataSPH 247Statistical Analysis ofLaboratory Data1May 7, 2010SPH 247 Statistical Analysis of Laboratory Data

2. Basic Design of Expression ArraysFor each gene that is a target for the array, we have a known DNA sequence.mRNA is reverse transcribed to DNA, and if a complementary sequence is on the on a chip, the DNA will be more likely to stickThe DNA is labeled with a dye that will fluoresce and generate a signal that is monotonic in the amount in the sampleMay 7, 2010SPH 247 Statistical Analysis of Laboratory Data2

3. May 7, 2010SPH 247 Statistical Analysis of Laboratory Data3TAAATCGATACGCATTAGTTCGACCTATCGAAGACCCAACACGGATTCGATACGTTAATATGACTACCTGCGCAACCCTAACGTCCATGTATCTAATACGATTTAGCTATGCGTAATCAAGCTGGATAGCTTCTGGGTTGTGCCTAAGCTATGCAATTATACTGATGGACGCGTTGGGATTGCAGGTACATAGATTATGCExonIntronProbe SequencecDNA arrays use variable length probes derived from expressed sequence tagsSpotted and almost always used with two color methodsCan be used in species with an unsequenced genomeLong oligoarrays use 60-70mersAgilent two-color arraysIllumina Bead ArraysUsually use computationally derived probes but can use probes from sequenced EST’s

4. Affymetrix GeneChips use multiple 25-mersFor each gene, one or more sets of 8-20 distinct probes May overlap May cover more than one exonAffymetrix chips also use mismatch (MM) probes that have the same sequence as perfect match probes except for the middle base which is changed to inhibitbinding.This is supposed to act as a control, but often instead binds to another mRNA species, so many analysts do not use themMay 7, 2010SPH 247 Statistical Analysis of Laboratory Data4

5. Illumina Bead ArraysBeads are coated with many copies of a 50-mer gene specific probe and a 29-mer address sequenceMultiple beads per probe, random, but around 20Each chip of the Ref-8 contains 8 arrays with ~ 25,000 targets, plus controlsEach chip of the WG-6 contains 6 arrays with ~ 50,000 targets, plus controlsMay 7, 2010SPH 247 Statistical Analysis of Laboratory Data5

6. Probe DesignA good probe sequence should match the chosen gene or exon from a gene and should not match any other gene in the genome.Melting temperature depends on the GC content and should be similar on all probes on an array since the hybridization must be conducted at a single temperature.May 7, 2010SPH 247 Statistical Analysis of Laboratory Data6

7. The affinity of a given piece of DNA for the probe sequence can depend on many things, including secondary and tertiary structure as well as GC content.This means that the relationship between the concentration of the RNA species in the original sample and the brightness of the spot on the array can be very different for different probes for the same gene.Thus only comparisons of intensity within the same probe across arrays makes sense.May 7, 2010SPH 247 Statistical Analysis of Laboratory Data7

8. Affymetrix GeneChipsFor each probe set, there are 8-20 perfect match (PM) probes which may overlap or not and which target the same geneThere are also mismatch (MM) probes which are supposed to serve as a control, but do so rather badlyMost of us ignore the MM probesMay 7, 2010SPH 247 Statistical Analysis of Laboratory Data8

9. Expression IndicesA key issue with Affymetrix chips is how to summarize the multiple data values on a chip for each probe set (aka gene).There have been a large number of suggested methods.Generally, the worst ones are those from Affy, by a long way; worse means less able to detect real differencesSummary of Illumina beads is simpler, but there are still issues.May 7, 2010SPH 247 Statistical Analysis of Laboratory Data9

10. Usable MethodsLi and Wong’s dCHIP and follow on work is demonstrably better than MAS 4.0 and MAS 5.0, but not as good as RMA and GLAThe RMA method of Irizarry et al. is available in Bioconductor.The GLA method (Durbin, Rocke, Zhou) is also available in Bioconductor/CRAN as part of the LMGene R packageMay 7, 2010SPH 247 Statistical Analysis of Laboratory Data10

11. Bioconductor Documentation> library(affy)Loading required package: BiobaseLoading required package: toolsWelcome to Bioconductor Vignettes contain introductory material. To view, type 'openVignette()'. To cite Bioconductor, see 'citation("Biobase")' and for packages 'citation(pkgname)'.Loading required package: affyioLoading required package: preprocessCoreMay 7, 2010SPH 247 Statistical Analysis of Laboratory Data11

12. Bioconductor Documentation> openVignette()Please select a vignette: 1: affy - 1. Primer 2: affy - 2. Built-in Processing Methods 3: affy - 3. Custom Processing Methods 4: affy - 4. Import Methods 5: affy - 5. Automatic downloading of CDF packages 6: Biobase - An introduction to Biobase and ExpressionSets 7: Biobase - Bioconductor Overview 8: Biobase - esApply Introduction 9: Biobase - Notes for eSet developers10: Biobase - Notes for writing introductory 'how to' documents11: Biobase - quick views of eSet instancesSelection: May 7, 2010SPH 247 Statistical Analysis of Laboratory Data12

13. Reading Affy Data into RThe CEL files contain the data from an array. We will look at data from an older type of array, the U95A which contains 12,625 probe sets and 409,600 probes.The CDF file contains information relating probe pair sets to locations on the array. These are built into the affy package for standard types.May 7, 2010SPH 247 Statistical Analysis of Laboratory Data13

14. Example Data SetData from Robert Rice’s lab on twelve keratinocyte cell lines, at six different stages.Affymetrix HG U95A GeneChips.For each “gene”, we will run a one-way ANOVA with two observations per cell.For this illustration, we will use RMA.May 7, 2010SPH 247 Statistical Analysis of Laboratory Data14

15. Files for the Analysis.CDF file has U95A chip definition (which probe is where on the chip). Built in to the affy package..CEL files contain the raw data after pixel level analysis, one number for each spot. Files are called LN0A.CEL, LN0B.CEL…LN5B.CEL and are on the web site.409,600 probe values in 12,625 probe sets.May 7, 2010SPH 247 Statistical Analysis of Laboratory Data15

16. The ReadAffy functionReadAffy() function reads all of the CEL files in the current working directory into an object of class AffyBatch, which is itself an object of class ExpressionSetReadAffy(widget=T) does so in a GUI that allows entry of other characteristics of the datasetYou can also specify filenames, phenotype or experimental data, and MIAME informationMay 7, 2010SPH 247 Statistical Analysis of Laboratory Data16

17. May 7, 2010SPH 247 Statistical Analysis of Laboratory Data17rrdata <- ReadAffy()> class(rrdata)[1] "AffyBatch"attr(,"package")[1] "affy“> dim(exprs(rrdata))[1] 409600 12> colnames(exprs(rrdata)) [1] "LN0A.CEL" "LN0B.CEL" "LN1A.CEL" "LN1B.CEL" "LN2A.CEL" "LN2B.CEL" [7] "LN3A.CEL" "LN3B.CEL" "LN4A.CEL" "LN4B.CEL" "LN5A.CEL" "LN5B.CEL"> length(probeNames(rrdata))[1] 201800> length(unique(probeNames(rrdata)))[1] 12625> length((featureNames(rrdata)))[1] 12625> featureNames(rrdata)[1:5][1] "100_g_at" "1000_at" "1001_at" "1002_f_at" "1003_s_at"

18. The ExpressionSet classAn object of class ExpressionSet has several slots the most important of which is an assayData object, containing one or more matrices. The best way to extract parts of this is using appropriate methods.exprs() extracts an expression matrixfeatureNames() extracts the names of the probe sets.May 7, 2010SPH 247 Statistical Analysis of Laboratory Data18

19. Expression IndicesThe 409,600 rows of the expression matrix in the AffyBatch object Data each correspond to a probe (25-mer)Ordinarily to use this we need to combine the probe level data for each probe set into a single expression numberThis has conceptually several stepsMay 7, 2010SPH 247 Statistical Analysis of Laboratory Data19

20. Steps in Expression Index ConstructionBackground correction is the process of adjusting the signals so that the zero point is similar on all parts of all arrays. We like to manage this so that zero signal after background correction corresponds approximately to zero amount of the mRNA species that is the target of the probe set.May 7, 2010SPH 247 Statistical Analysis of Laboratory Data20

21. Data transformation is the process of changing the scale of the data so that it is more comparable from high to low. Common transformations are the logarithm and generalized logarithmNormalization is the process of adjusting for systematic differences from one array to another.Normalization may be done before or after transformation, and before or after probe set summarization.May 7, 2010SPH 247 Statistical Analysis of Laboratory Data21

22. One may use only the perfect match (PM) probes, or may subtract or otherwise use the mismatch (MM) probesThere are many ways to summarize 20 PM probes and 20 MM probes on 10 arrays (total of 200 numbers) into 10 expression index numbersMay 7, 2010SPH 247 Statistical Analysis of Laboratory Data22

23. May 7, 2010SPH 247 Statistical Analysis of Laboratory Data230110100Mean200618_at1360216158198233.0200618_at2313402106103231.0200618_at31301827991120.5200618_at4351370195136263.0200618_at516413098107124.8200618_at6223219164196200.5200618_at7437529195158329.8200618_at8509554274128366.3200618_at9522720285198431.3200618_at10668715247260472.5200618_at11306286144159223.8ExpressionIndex362.1393.0176.8157.6Probe intensities for LASP1 in a radiationdose-response experiment

24. May 7, 2010SPH 247 Statistical Analysis of Laboratory Data24Log probe intensities for LASP1 in a radiationdose-response experiment0110100Mean200618_at12.562.332.202.302.35200618_at22.502.602.032.012.28200618_at32.112.261.901.962.06200618_at42.552.572.292.132.38200618_at52.212.111.992.032.09200618_at62.352.342.212.292.30200618_at72.642.722.292.202.46200618_at82.712.742.442.112.50200618_at92.722.862.452.302.58200618_at102.822.852.392.412.62200618_at112.492.462.162.202.33ExpressionIndex2.512.532.212.18

25. The RMA MethodBackground correction that does not make 0 signal correspond to 0 amountQuantile normalizationLog2 transformMedian polish summary of PM probesMay 7, 2010SPH 247 Statistical Analysis of Laboratory Data25

26. May 7, 2010SPH 247 Statistical Analysis of Laboratory Data26> eset <- rma(rrdata)trying URL 'http://bioconductor.org/packages/2.1/…Content type 'application/zip' length 1352776 bytes (1.3 Mb)opened URLdownloaded 1.3 Mbpackage 'hgu95av2cdf' successfully unpacked and MD5 sums checkedThe downloaded packages are in C:\Documents and Settings\dmrocke\Local Settings…updating HTML package descriptionsBackground correctingNormalizingCalculating Expression> class(eset)[1] "ExpressionSet"attr(,"package")[1] "Biobase"> dim(exprs(eset))[1] 12625 12> featureNames(eset)[1:5][1] "100_g_at" "1000_at" "1001_at" "1002_f_at" "1003_s_at"

27. May 7, 2010SPH 247 Statistical Analysis of Laboratory Data27> exprs(eset)[1:5,] LN0A.CEL LN0B.CEL LN1A.CEL LN1B.CEL LN2A.CEL LN2B.CEL LN3A.CEL100_g_at 9.195937 9.388350 9.443115 9.012228 9.311773 9.386037 9.3860891000_at 8.229724 7.790238 7.733320 7.864438 7.620704 7.930373 7.5027591001_at 5.066185 5.057729 4.940588 4.839563 4.808808 5.195664 4.9528831002_f_at 5.409422 5.472210 5.419907 5.343012 5.266068 5.442173 5.1904401003_s_at 7.262739 7.323087 7.355976 7.221642 7.023408 7.165052 7.011527 LN3B.CEL LN4A.CEL LN4B.CEL LN5A.CEL LN5B.CEL100_g_at 9.394606 9.602404 9.711533 9.826789 9.6455651000_at 7.463158 7.644588 7.497006 7.618449 7.7101101001_at 4.871329 4.875907 4.853802 4.752610 4.8343171002_f_at 5.200380 5.436028 5.310046 5.300938 5.4278411003_s_at 7.185894 7.235551 7.292139 7.218818 7.253799

28. May 7, 2010SPH 247 Statistical Analysis of Laboratory Data28> summary(exprs(eset)) LN0A.CEL LN0B.CEL LN1A.CEL LN1B.CEL Min. : 2.713 Min. : 2.585 Min. : 2.611 Min. : 2.636 1st Qu.: 4.478 1st Qu.: 4.449 1st Qu.: 4.458 1st Qu.: 4.477 Median : 6.080 Median : 6.072 Median : 6.070 Median : 6.078 Mean : 6.120 Mean : 6.124 Mean : 6.120 Mean : 6.128 3rd Qu.: 7.443 3rd Qu.: 7.473 3rd Qu.: 7.467 3rd Qu.: 7.467 Max. :12.042 Max. :12.146 Max. :12.122 Max. :11.889 LN2A.CEL LN2B.CEL LN3A.CEL LN3B.CEL Min. : 2.598 Min. : 2.717 Min. : 2.633 Min. : 2.622 1st Qu.: 4.444 1st Qu.: 4.469 1st Qu.: 4.425 1st Qu.: 4.428 Median : 6.008 Median : 6.058 Median : 6.017 Median : 6.028 Mean : 6.109 Mean : 6.125 Mean : 6.116 Mean : 6.117 3rd Qu.: 7.426 3rd Qu.: 7.422 3rd Qu.: 7.444 3rd Qu.: 7.459 Max. :13.135 Max. :13.110 Max. :13.106 Max. :13.138 LN4A.CEL LN4B.CEL LN5A.CEL LN5B.CEL Min. : 2.742 Min. : 2.634 Min. : 2.615 Min. : 2.590 1st Qu.: 4.468 1st Qu.: 4.433 1st Qu.: 4.448 1st Qu.: 4.487 Median : 6.074 Median : 6.050 Median : 6.053 Median : 6.068 Mean : 6.122 Mean : 6.120 Mean : 6.121 Mean : 6.123 3rd Qu.: 7.460 3rd Qu.: 7.478 3rd Qu.: 7.477 3rd Qu.: 7.457 Max. :12.033 Max. :12.162 Max. :11.925 Max. :11.952

29. Probe Sets not GenesIt is unavoidable to refer to a probe set as measuring a “gene”, but nevertheless it can be deceptiveThe annotation of a probe set may be based on homology with a gene of possibly known function in a different organismOnly a relatively few probe sets correspond to genes with known function and known structure in the organism being studiedMay 7, 2010SPH 247 Statistical Analysis of Laboratory Data29

30. ExerciseDownload the ten arrays from the web siteLoad the arrays into R using Read.Affy and construct the RMA expression indicesMay 7, 2010SPH 247 Statistical Analysis of Laboratory Data30