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Analysis of Affymetrix and Illumina Array Data

Analysis of Affymetrix and Illumina Array Data. SPH 247 Statistical Analysis of Laboratory Data. Basic Design of Expression Arrays. For each gene that is a target for the array, we have a known DNA sequence.

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Analysis of Affymetrix and Illumina Array Data

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  1. Analysis of Affymetrix and Illumina Array Data SPH 247 Statistical Analysis of Laboratory Data SPH 247 Statistical Analysis of Laboratory Data

  2. Basic Design of Expression Arrays • For 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 stick • The DNA is labeled with a dye that will fluoresce and generate a signal that is monotonic in the amount in the sample SPH 247 Statistical Analysis of Laboratory Data

  3. Intron Exon TAAATCGATACGCATTAGTTCGACCTATCGAAGACCCAACACGGATTCGATACGTTAATATGACTACCTGCGCAACCCTAACGTCCATGTATCTAATACG ATTTAGCTATGCGTAATCAAGCTGGATAGCTTCTGGGTTGTGCCTAAGCTATGCAATTATACTGATGGACGCGTTGGGATTGCAGGTACATAGATTATGC Probe Sequence • cDNA arrays use variable length probes derived from expressed sequence tags • Spotted and almost always used with two color methods • Can be used in species with an unsequenced genome • Long oligoarrays use 60-70mers • Agilent two-color arrays • Illumina Bead Arrays • Usually use computationally derived probes but can use probes from sequenced EST’s SPH 247 Statistical Analysis of Laboratory Data

  4. AffymetrixGeneChips use multiple 25-mers • For each gene, one or more sets of 8-20 distinct probes • May overlap • May cover more than one exon • Affymetrix 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 them SPH 247 Statistical Analysis of Laboratory Data

  5. Illumina Bead Arrays • Beads are coated with many copies of a 50-mer gene specific probe and a 29-mer address sequence • Multiple beads per probe, random, but around 20 • Each chip of the Ref-8 contains 8 arrays with ~ 25,000 targets, plus controls • Each chip of the WG-6 contains 6 arrays with ~ 50,000 targets, plus controls SPH 247 Statistical Analysis of Laboratory Data

  6. Probe Design • A 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. SPH 247 Statistical Analysis of Laboratory Data

  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. SPH 247 Statistical Analysis of Laboratory Data

  8. Affymetrix GeneChips • For each probe set, there are 8-20 perfect match (PM) probes which may overlap or not and which target the same gene • There are also mismatch (MM) probes which are supposed to serve as a control, but do so rather badly • Most of us ignore the MM probes SPH 247 Statistical Analysis of Laboratory Data

  9. Expression Indices • A 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 differences • Summary of Illumina beads is simpler, but there are still issues. SPH 247 Statistical Analysis of Laboratory Data

  10. Usable Methods • Li 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 GLA • The 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 LMGeneR package SPH 247 Statistical Analysis of Laboratory Data

  11. Bioconductor Documentation > library(affy) Loading required package: Biobase Loading required package: tools Welcome to Bioconductor Vignettes contain introductory material. To view, type 'openVignette()'. To cite Bioconductor, see 'citation("Biobase")' and for packages 'citation(pkgname)'. Loading required package: affyio Loading required package: preprocessCore SPH 247 Statistical Analysis of Laboratory Data

  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 developers 10: Biobase - Notes for writing introductory 'how to' documents 11: Biobase - quick views of eSet instances Selection: SPH 247 Statistical Analysis of Laboratory Data

  13. Reading Affy Data into R • The 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. SPH 247 Statistical Analysis of Laboratory Data

  14. Example Data Set • Data 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. SPH 247 Statistical Analysis of Laboratory Data

  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. SPH 247 Statistical Analysis of Laboratory Data

  16. The ReadAffy function • ReadAffy() 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 ExpressionSet • ReadAffy(widget=T) does so in a GUI that allows entry of other characteristics of the dataset • You can also specify filenames, phenotype or experimental data, and MIAME information SPH 247 Statistical Analysis of Laboratory Data

  17. rrdata <- 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" SPH 247 Statistical Analysis of Laboratory Data

  18. The ExpressionSet class • An object of class ExpressionSethas 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 matrix • featureNames() extracts the names of the probe sets. SPH 247 Statistical Analysis of Laboratory Data

  19. Expression Indices • The 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 number • This has conceptually several steps SPH 247 Statistical Analysis of Laboratory Data

  20. Steps in Expression Index Construction • Background 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. SPH 247 Statistical Analysis of Laboratory Data

  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 logarithm • Normalization 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. SPH 247 Statistical Analysis of Laboratory Data

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

  23. Probe intensities for LASP1 in a radiation dose-response experiment SPH 247 Statistical Analysis of Laboratory Data

  24. Log probe intensities for LASP1 in a radiation dose-response experiment SPH 247 Statistical Analysis of Laboratory Data

  25. The RMA Method • Background correction that does not make 0 signal correspond to 0 amount • Quantile normalization • Log2 transform • Median polish summary of PM probes SPH 247 Statistical Analysis of Laboratory Data

  26. > eset <- rma(rrdata) trying URL 'http://bioconductor.org/packages/2.1/… Content type 'application/zip' length 1352776 bytes (1.3 Mb) opened URL downloaded 1.3 Mb package 'hgu95av2cdf' successfully unpacked and MD5 sums checked The downloaded packages are in C:\Documents and Settings\dmrocke\Local Settings… updating HTML package descriptions Background correcting Normalizing Calculating 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" SPH 247 Statistical Analysis of Laboratory Data

  27. > exprs(eset)[1:5,] LN0A.CEL LN0B.CEL LN1A.CEL LN1B.CEL LN2A.CEL LN2B.CEL LN3A.CEL 100_g_at 9.195937 9.388350 9.443115 9.012228 9.311773 9.386037 9.386089 1000_at 8.229724 7.790238 7.733320 7.864438 7.620704 7.930373 7.502759 1001_at 5.066185 5.057729 4.940588 4.839563 4.808808 5.195664 4.952883 1002_f_at 5.409422 5.472210 5.419907 5.343012 5.266068 5.442173 5.190440 1003_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.CEL 100_g_at 9.394606 9.602404 9.711533 9.826789 9.645565 1000_at 7.463158 7.644588 7.497006 7.618449 7.710110 1001_at 4.871329 4.875907 4.853802 4.752610 4.834317 1002_f_at 5.200380 5.436028 5.310046 5.300938 5.427841 1003_s_at 7.185894 7.235551 7.292139 7.218818 7.253799 SPH 247 Statistical Analysis of Laboratory Data

  28. > 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 SPH 247 Statistical Analysis of Laboratory Data

  29. Probe Sets not Genes • It is unavoidable to refer to a probe set as measuring a “gene”, but nevertheless it can be deceptive • The annotation of a probe set may be based on homology with a gene of possibly known function in a different organism • Only a relatively few probe sets correspond to genes with known function and known structure in the organism being studied SPH 247 Statistical Analysis of Laboratory Data

  30. Exercise • Download the ten arrays from the web site • Load the arrays into R using Read.Affy and construct the RMA expression indices SPH 247 Statistical Analysis of Laboratory Data

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