1. Analysis of Affymetrix GeneChip Data
11/28/2018
Analysis of Affymetrix GeneChip Data
EPP 245
Statistical Analysis of
Laboratory Data

2. Basic Design of Expression Arrays
11/28/2018
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
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EPP 245 Statistical Analysis of Laboratory Data

3. EPP 245 Statistical Analysis of Laboratory Data
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Exon
Intron
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
Spotted arrays from UC Davis or elsewhere
Usually use computationally derived probes but can use probes from sequenced EST’s
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EPP 245 Statistical Analysis of Laboratory Data

4. EPP 245 Statistical Analysis of Laboratory Data
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Affymetrix GeneChips 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 inhibit binding.
This is supposed to act as a control, but often instead binds to another mRNA species, so many analysts do not use them
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EPP 245 Statistical Analysis of Laboratory Data

5. EPP 245 Statistical Analysis of Laboratory Data
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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.
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EPP 245 Statistical Analysis of Laboratory Data

6. EPP 245 Statistical Analysis of Laboratory Data
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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.
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EPP 245 Statistical Analysis of Laboratory Data

7. EPP 245 Statistical Analysis of Laboratory Data
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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
November 15, 2007
EPP 245 Statistical Analysis of Laboratory Data

8. EPP 245 Statistical Analysis of Laboratory Data
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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
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EPP 245 Statistical Analysis of Laboratory Data

9. EPP 245 Statistical Analysis of Laboratory Data
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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
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EPP 245 Statistical Analysis of Laboratory Data

10. Bioconductor Documentation
11/28/2018
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
November 15, 2007
EPP 245 Statistical Analysis of Laboratory Data

11. Bioconductor Documentation
11/28/2018
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:
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EPP 245 Statistical Analysis of Laboratory Data

12. Reading Affy Data into R
11/28/2018
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.
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EPP 245 Statistical Analysis of Laboratory Data

13. EPP 245 Statistical Analysis of Laboratory Data
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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.
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EPP 245 Statistical Analysis of Laboratory Data

14. EPP 245 Statistical Analysis of Laboratory Data
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Files for the Analysis
.CDF file has U95A chip definition (which probe is where on the chip). Built in.
.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.
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15. EPP 245 Statistical Analysis of Laboratory Data
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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
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16. EPP 245 Statistical Analysis of Laboratory Data
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rrdata <- ReadAffy()
> class(rrdata)
[1] "AffyBatch"
attr(,"package")
[1] "affy“
> dim(exprs(rrdata))
[1]
> 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]
> length(unique(probeNames(rrdata)))
[1] 12625
> length((featureNames(rrdata)))
> featureNames(rrdata)[1:5]
[1] "100_g_at" "1000_at" "1001_at" "1002_f_at" "1003_s_at"
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EPP 245 Statistical Analysis of Laboratory Data

17. The ExpressionSet class
11/28/2018
The ExpressionSet class
An 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 matrix
featureNames() extracts the names of the probe sets.
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18. EPP 245 Statistical Analysis of Laboratory Data
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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
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EPP 245 Statistical Analysis of Laboratory Data

19. Steps in Expression Index Construction
11/28/2018
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.
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EPP 245 Statistical Analysis of Laboratory Data

20. EPP 245 Statistical Analysis of Laboratory Data
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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.
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EPP 245 Statistical Analysis of Laboratory Data

21. EPP 245 Statistical Analysis of Laboratory Data
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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
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EPP 245 Statistical Analysis of Laboratory Data

22. Probe intensities for LASP1 in a radiation dose-response experiment
11/28/2018
Probe intensities for LASP1 in a radiation
dose-response experiment 1 10
100
Mean
200618_at1
360
216
158
198
233.0
200618_at2
313
402
106
103
231.0
200618_at3
130
182 79 91
120.5
200618_at4
351
370
195
136
263.0
200618_at5
164 98
107
124.8
200618_at6
223
219
196
200.5
200618_at7
437
529
329.8
200618_at8
509
554
274
128
366.3
200618_at9
522
720
285
431.3
200618_at10
668
715
247
260
472.5
200618_at11
306
286
144
159
223.8
Expression
Index
362.1
393.0
176.8
157.6
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EPP 245 Statistical Analysis of Laboratory Data

23. Log probe intensities for LASP1 in a radiation
11/28/2018
Log probe intensities for LASP1 in a radiation
dose-response experiment 1 10
100
Mean
200618_at1
2.56
2.33
2.20
2.30
2.35
200618_at2
2.50
2.60
2.03
2.01
2.28
200618_at3
2.11
2.26
1.90
1.96
2.06
200618_at4
2.55
2.57
2.29
2.13
2.38
200618_at5
2.21
1.99
2.09
200618_at6
2.34
200618_at7
2.64
2.72
2.46
200618_at8
2.71
2.74
2.44
200618_at9
2.86
2.45
2.58
200618_at10
2.82
2.85
2.39
2.41
2.62
200618_at11
2.49
2.16
Expression
Index
2.51
2.53
2.18
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24. EPP 245 Statistical Analysis of Laboratory Data
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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
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EPP 245 Statistical Analysis of Laboratory Data

25. EPP 245 Statistical Analysis of Laboratory Data
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> eset <- rma(rrdata)
trying URL '
Content type 'application/zip' length 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]
> featureNames(eset)[1:5]
[1] "100_g_at" "1000_at" "1001_at" "1002_f_at" "1003_s_at"
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EPP 245 Statistical Analysis of Laboratory Data

26. EPP 245 Statistical Analysis of Laboratory Data
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> exprs(eset)[1:5,]
LN0A.CEL LN0B.CEL LN1A.CEL LN1B.CEL LN2A.CEL LN2B.CEL LN3A.CEL
100_g_at
1000_at
1001_at
1002_f_at
1003_s_at
LN3B.CEL LN4A.CEL LN4B.CEL LN5A.CEL LN5B.CEL
100_g_at
1000_at
1001_at
1002_f_at
1003_s_at
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EPP 245 Statistical Analysis of Laboratory Data

27. EPP 245 Statistical Analysis of Laboratory Data
> summary(exprs(eset))
LN0A.CEL LN0B.CEL LN1A.CEL LN1B.CEL
Min. : Min. : Min. : Min. : 2.636
1st Qu.: st Qu.: st Qu.: st Qu.: 4.477
Median : Median : Median : Median : 6.078
Mean : Mean : Mean : Mean : 6.128
3rd Qu.: rd Qu.: rd Qu.: rd Qu.: 7.467
Max. : Max. : Max. : Max. :11.889
LN2A.CEL LN2B.CEL LN3A.CEL LN3B.CEL
Min. : Min. : Min. : Min. : 2.622
1st Qu.: st Qu.: st Qu.: st Qu.: 4.428
Median : Median : Median : Median : 6.028
Mean : Mean : Mean : Mean : 6.117
3rd Qu.: rd Qu.: rd Qu.: rd Qu.: 7.459
Max. : Max. : Max. : Max. :13.138
LN4A.CEL LN4B.CEL LN5A.CEL LN5B.CEL
Min. : Min. : Min. : Min. : 2.590
1st Qu.: st Qu.: st Qu.: st Qu.: 4.487
Median : Median : Median : Median : 6.068
Mean : Mean : Mean : Mean : 6.123
3rd Qu.: rd Qu.: rd Qu.: rd Qu.: 7.457
Max. : Max. : Max. : Max. :11.952
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28. EPP 245 Statistical Analysis of Laboratory Data
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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
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EPP 245 Statistical Analysis of Laboratory Data

29. EPP 245 Statistical Analysis of Laboratory Data
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Exercise
Download the ten arrays from the web site
Load the arrays into R using Read.Affy and construct the RMA expression indices
November 15, 2007
EPP 245 Statistical Analysis of Laboratory Data