Chromatin Immuno-precipitation (CHIP)-chip Analysis 11/07/07.
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Transcript of Chromatin Immuno-precipitation (CHIP)-chip Analysis 11/07/07.
![Page 1: Chromatin Immuno-precipitation (CHIP)-chip Analysis 11/07/07.](https://reader038.fdocuments.net/reader038/viewer/2022103022/56649cdf5503460f949a8af4/html5/thumbnails/1.jpg)
Chromatin Immuno-precipitation (CHIP)-chip Analysis
11/07/07
![Page 2: Chromatin Immuno-precipitation (CHIP)-chip Analysis 11/07/07.](https://reader038.fdocuments.net/reader038/viewer/2022103022/56649cdf5503460f949a8af4/html5/thumbnails/2.jpg)
Experimental Protocol
• Step 1: crosslink protein with DNA
• Step 2: sonication (break) DNA
Kim and Ren 2007
![Page 3: Chromatin Immuno-precipitation (CHIP)-chip Analysis 11/07/07.](https://reader038.fdocuments.net/reader038/viewer/2022103022/56649cdf5503460f949a8af4/html5/thumbnails/3.jpg)
Experimental Protocol
• Step 1: crosslink– fix protein with DNA
• Step 2: sonication– break DNA
• Step 3: immuno-precipitation– Pull down target
protein by specific antibody
Kim and Ren 2007
![Page 4: Chromatin Immuno-precipitation (CHIP)-chip Analysis 11/07/07.](https://reader038.fdocuments.net/reader038/viewer/2022103022/56649cdf5503460f949a8af4/html5/thumbnails/4.jpg)
Experimental Protocol
• Step 1: crosslink– fix protein with DNA
• Step 2: sonication– break DNA
• Step 3: immuno-precipitation– Pull down target protein by
specific antibody
• Step 4: hybridization– Hybridize input and pulled-
down DNA on microarray
Kim and Ren 2007
![Page 5: Chromatin Immuno-precipitation (CHIP)-chip Analysis 11/07/07.](https://reader038.fdocuments.net/reader038/viewer/2022103022/56649cdf5503460f949a8af4/html5/thumbnails/5.jpg)
Intergenic microarray
• Array probes are PCR products of intergenic regions.
• Binding signal is represented by a single probe.
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ChIP-array
• Consistently enriched in repeated ChIP-arrays are selected to be the TF binding targets
• Usually hundreds of targets, each ~1000 long
• We want to know the precise binding
(e.g. 10 bases)
TF Target
![Page 7: Chromatin Immuno-precipitation (CHIP)-chip Analysis 11/07/07.](https://reader038.fdocuments.net/reader038/viewer/2022103022/56649cdf5503460f949a8af4/html5/thumbnails/7.jpg)
• Microarray probes are oligonucleotide sequences with regular spacing covering a whole genomic region.
chromosome
Tiling arrays
![Page 8: Chromatin Immuno-precipitation (CHIP)-chip Analysis 11/07/07.](https://reader038.fdocuments.net/reader038/viewer/2022103022/56649cdf5503460f949a8af4/html5/thumbnails/8.jpg)
Tiling Array Data
Each TF binding signal is represented by multiple probes.
Need more sophisticated statistical tools.Kim and Ren 2007
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Methods
• Moving average t-test (Keles et al. 2004)
• HMM (Li et al. 2005; Yuan et al. 2005)
• Tilemap (Ji and Wong 2005)
• MAT (Johnson et al. 2006)
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Keles’ method• Calculate a two-sample t-
statistic Y2
Y1
i
CHIP-signal
Input-signal
22,21
2,1
,1,2,
/ˆ/ˆ nn
YYT
ii
iini
Keles et al. 2004
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Keles’ method• Calculate a two-sample t-
statistic Y2
Y1
i
CHIP-signal
Input-signal
22,21
2,1
,1,2,
/ˆ/ˆ nn
YYT
ii
iini
w
1
,*,
1 wi
ihnhni T
wT
• Moving average scan-statistic
![Page 12: Chromatin Immuno-precipitation (CHIP)-chip Analysis 11/07/07.](https://reader038.fdocuments.net/reader038/viewer/2022103022/56649cdf5503460f949a8af4/html5/thumbnails/12.jpg)
Multiple hypothesis testing
• Multiple hypothesis testing needs to be considered to control false positive error rates.
• What is the null distribution of this statistic?
1
,*,
1 wi
ihnhni T
wT
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Multiple hypothesis testing
• Assume has t-distribution• Approximate
by normal distribution.
• Alternatively can use resampling method to estimate the null distribution.
nhT ,
1
,*,
1 wi
ihnhni T
wT
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Tilemap
Improvement over Keles’ method in following ways
• Use a more robust test statistic
• Estimate the null distribution without prior assumptions.
Ji and Wong 2005
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Step 1: calculating a t-like test statistic
• Model:
log-intensity
Probe index Condition index Replicate index
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Step 1: calculating a t-like test statistic
• Model:
log-intensity
pooling data
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• Two samples:
• Multiple samples:
Step 1: calculating a t-like test statistic
• Want to have a robust estimate of variance.
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Notation
Step 1: calculating a t-like test statistic
Estimation of by variance shrinkage
Shrinkage factor
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Step 2: Merging data
• Moving average
• Alternatively use Hidden Markov Model
![Page 20: Chromatin Immuno-precipitation (CHIP)-chip Analysis 11/07/07.](https://reader038.fdocuments.net/reader038/viewer/2022103022/56649cdf5503460f949a8af4/html5/thumbnails/20.jpg)
Step 3: control FDR
Goal: To find null and signal distributions
Idea: assume a mixture modelThis is unidentifiable!
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Step 3: control FDR
Goal: To find null and signal distributions
Idea: assume a mixture modelThis is unidentifiable!
A clever trick: Look for
with
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How to find g0 and g1
• To get g1, can we select probes with highest t-score?
• Why or why not?
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How to find g0 and g1
• Idea: signals at neighboring probes are correlated, whereas noises are not (hopefully!)
• First select probes that have the highest t-score ti.
• Use their downstream value ti+1 to estimate g1.
• Use same trick to estimate g0.
![Page 24: Chromatin Immuno-precipitation (CHIP)-chip Analysis 11/07/07.](https://reader038.fdocuments.net/reader038/viewer/2022103022/56649cdf5503460f949a8af4/html5/thumbnails/24.jpg)
Step 3: control FDR
Goal: To find null and signal distributions
Idea: assume a mixture modelThis is unidentifiable!
A clever trick: Find
with
Additional assumption:
![Page 25: Chromatin Immuno-precipitation (CHIP)-chip Analysis 11/07/07.](https://reader038.fdocuments.net/reader038/viewer/2022103022/56649cdf5503460f949a8af4/html5/thumbnails/25.jpg)
Step 3: control FDR
Goal: To find null and signal distributions
Idea: assume a mixture modelThis is unidentifiable!
A clever trick: Find
with
Additional assumption:
![Page 26: Chromatin Immuno-precipitation (CHIP)-chip Analysis 11/07/07.](https://reader038.fdocuments.net/reader038/viewer/2022103022/56649cdf5503460f949a8af4/html5/thumbnails/26.jpg)
Step 3: Unbalanced mixture score
with
)()( 00 tgtf
is estimated by fitting
dttftg
dttftgtfth2
10
101
0)()(
)()())()(̂
![Page 27: Chromatin Immuno-precipitation (CHIP)-chip Analysis 11/07/07.](https://reader038.fdocuments.net/reader038/viewer/2022103022/56649cdf5503460f949a8af4/html5/thumbnails/27.jpg)
False discovery rate (FDR)
Determine TF bindings sites are FDR cutoff
![Page 28: Chromatin Immuno-precipitation (CHIP)-chip Analysis 11/07/07.](https://reader038.fdocuments.net/reader038/viewer/2022103022/56649cdf5503460f949a8af4/html5/thumbnails/28.jpg)
How to find g0 and g1
• Idea: signals at neighboring probes are correlated, whereas noises are not (hopefully!)
• First select probes that have the highest t-score ti.
• Use their downstream value ti+1 to estimate g1.
• Use same trick to estimate g0.
Memory problem!
![Page 29: Chromatin Immuno-precipitation (CHIP)-chip Analysis 11/07/07.](https://reader038.fdocuments.net/reader038/viewer/2022103022/56649cdf5503460f949a8af4/html5/thumbnails/29.jpg)
Example: Analysis of a cMyc binding data
![Page 30: Chromatin Immuno-precipitation (CHIP)-chip Analysis 11/07/07.](https://reader038.fdocuments.net/reader038/viewer/2022103022/56649cdf5503460f949a8af4/html5/thumbnails/30.jpg)
Comparison of models
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Simulation results
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MAT
Basic Idea:
• Baseline level correction
• Standardize probe intensity with respect to the expected baseline value
(Johnson et al. 2006)
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MAT
• How to estimate the baseline values?
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Estimated nucleotide effect
A C
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MAT
• Standardization
binaffinity
ˆ)log(
i
iii s
mPMt
region)in values()( tTMnregionMATscore p
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(X.S. Liu)
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Reading List
• Keles el 2004– Developed a multiple hypothesis method for
tiling array analysis
• Ji and Wong 2005– Tilemap; improved over Keles et al.’s method
• Johnson et al. 2006– MAT: showed baseline adjustment improved
signal detection.