Transcript Multidisciplinary COllaboration: Why and How?

Analysis of Affymetrix
GeneChip Data
EPP 245
Statistical Analysis of
Laboratory Data
1
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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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
– Spotted arrays from UC Davis or elsewhere
– Usually use computationally derived probes but can use probes
from sequenced EST’s
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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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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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• 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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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
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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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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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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
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Bioconductor Documentation
> openVignette()
Please select a vignette:
1:
2:
3:
4:
5:
6:
7:
8:
9:
10:
11:
affy - 1.
affy - 2.
affy - 3.
affy - 4.
affy - 5.
Biobase Biobase Biobase Biobase Biobase Biobase -
Primer
Built-in Processing Methods
Custom Processing Methods
Import Methods
Automatic downloading of CDF packages
An introduction to Biobase and ExpressionSets
Bioconductor Overview
esApply Introduction
Notes for eSet developers
Notes for writing introductory 'how to' documents
quick views of eSet instances
Selection:
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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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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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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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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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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"
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"1002_f_at" "1003_s_at"
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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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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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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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• 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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• 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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Probe intensities for LASP1 in a radiation
dose-response experiment
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Mean
0
1
10
100
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
130
98
107
124.8
200618_at6
223
219
164
196
200.5
200618_at7
437
529
195
158
329.8
200618_at8
509
554
274
128
366.3
200618_at9
522
720
285
198
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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Log probe intensities for LASP1 in a radiation
dose-response experiment
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Mean
0
1
10
100
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
2.11
1.99
2.03
2.09
200618_at6
2.35
2.34
2.21
2.29
2.30
200618_at7
2.64
2.72
2.29
2.20
2.46
200618_at8
2.71
2.74
2.44
2.11
2.50
200618_at9
2.72
2.86
2.45
2.30
2.58
200618_at10
2.82
2.85
2.39
2.41
2.62
200618_at11
2.49
2.46
2.16
2.20
2.33
Expression
Index
2.51
2.53
2.21
2.18
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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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> 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"
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"1001_at"
"1002_f_at" "1003_s_at"
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> exprs(eset)[1:5,]
LN0A.CEL LN0B.CEL
100_g_at 9.195937 9.388350
1000_at
8.229724 7.790238
1001_at
5.066185 5.057729
1002_f_at 5.409422 5.472210
1003_s_at 7.262739 7.323087
LN3B.CEL LN4A.CEL
100_g_at 9.394606 9.602404
1000_at
7.463158 7.644588
1001_at
4.871329 4.875907
1002_f_at 5.200380 5.436028
1003_s_at 7.185894 7.235551
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LN1A.CEL
9.443115
7.733320
4.940588
5.419907
7.355976
LN4B.CEL
9.711533
7.497006
4.853802
5.310046
7.292139
LN1B.CEL
9.012228
7.864438
4.839563
5.343012
7.221642
LN5A.CEL
9.826789
7.618449
4.752610
5.300938
7.218818
EPP 245 Statistical Analysis of
Laboratory Data
LN2A.CEL
9.311773
7.620704
4.808808
5.266068
7.023408
LN5B.CEL
9.645565
7.710110
4.834317
5.427841
7.253799
LN2B.CEL
9.386037
7.930373
5.195664
5.442173
7.165052
LN3A.CEL
9.386089
7.502759
4.952883
5.190440
7.011527
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> summary(exprs(eset))
LN0A.CEL
LN0B.CEL
Min.
: 2.713
Min.
: 2.585
1st Qu.: 4.478
1st Qu.: 4.449
Median : 6.080
Median : 6.072
Mean
: 6.120
Mean
: 6.124
3rd Qu.: 7.443
3rd Qu.: 7.473
Max.
:12.042
Max.
:12.146
LN2A.CEL
LN2B.CEL
Min.
: 2.598
Min.
: 2.717
1st Qu.: 4.444
1st Qu.: 4.469
Median : 6.008
Median : 6.058
Mean
: 6.109
Mean
: 6.125
3rd Qu.: 7.426
3rd Qu.: 7.422
Max.
:13.135
Max.
:13.110
LN4A.CEL
LN4B.CEL
Min.
: 2.742
Min.
: 2.634
1st Qu.: 4.468
1st Qu.: 4.433
Median : 6.074
Median : 6.050
Mean
: 6.122
Mean
: 6.120
3rd Qu.: 7.460
3rd Qu.: 7.478
Max.
:12.033
Max.
:12.162
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LN1A.CEL
Min.
: 2.611
1st Qu.: 4.458
Median : 6.070
Mean
: 6.120
3rd Qu.: 7.467
Max.
:12.122
LN3A.CEL
Min.
: 2.633
1st Qu.: 4.425
Median : 6.017
Mean
: 6.116
3rd Qu.: 7.444
Max.
:13.106
LN5A.CEL
Min.
: 2.615
1st Qu.: 4.448
Median : 6.053
Mean
: 6.121
3rd Qu.: 7.477
Max.
:11.925
EPP 245 Statistical Analysis of
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LN1B.CEL
Min.
: 2.636
1st Qu.: 4.477
Median : 6.078
Mean
: 6.128
3rd Qu.: 7.467
Max.
:11.889
LN3B.CEL
Min.
: 2.622
1st Qu.: 4.428
Median : 6.028
Mean
: 6.117
3rd Qu.: 7.459
Max.
:13.138
LN5B.CEL
Min.
: 2.590
1st Qu.: 4.487
Median : 6.068
Mean
: 6.123
3rd Qu.: 7.457
Max.
:11.952
27
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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Exercise
• Download the ten arrays from the web site
• Load the arrays into R using Read.Affy
and construct the RMA expression indices
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