1. Chromatin Immuno-precipitation (CHIP)-chip Analysis
11/07/07

2. Experimental Protocol
Step 1: crosslink protein with DNA
Step 2: sonication (break) DNA
Kim and Ren 2007

3. 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

4. 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

5. Intergenic microarray
Array probes are PCR products of intergenic regions.
Binding signal is represented by a single probe.

6. 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

7. Tiling arrays
Microarray probes are oligonucleotide sequences with regular spacing covering a whole genomic region.
chromosome

8. Tiling Array Data
Each TF binding signal is represented by multiple probes.
Need more sophisticated statistical tools.
Kim and Ren 2007

9. 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)

10. Keles’ method Calculate a two-sample t-statistic CHIP-signal Y2 Y1
Input-signal i
Keles et al. 2004

11. Keles’ method Calculate a two-sample t-statistic
CHIP-signal Y2 Y1
Moving average scan-statistic
Input-signal i

12. Multiple hypothesis testing
Multiple hypothesis testing needs to be considered to control false positive error rates.
What is the null distribution of this statistic?

13. Multiple hypothesis testing
Assume has t-distribution
Approximate
by normal distribution.
Alternatively can use resampling method to estimate the null distribution.

14. 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

15. Step 1: calculating a t-like test statistic
Model:
log-intensity
Probe index
Condition index
Replicate index

16. Step 1: calculating a t-like test statistic
Model:
log-intensity
pooling data

17. Step 1: calculating a t-like test statistic
Two samples:
Multiple samples:
Want to have a robust estimate of variance.

18. Step 1: calculating a t-like test statistic
Estimation of by variance shrinkage
Shrinkage factor
Notation

19. Step 2: Merging data Moving average
Alternatively use Hidden Markov Model

20. Step 3: control FDR Goal: To find null and signal distributions
Idea: assume a mixture model
This is unidentifiable!

21. Step 3: control FDR Goal: To find null and signal distributions
Idea: assume a mixture model
This is unidentifiable!
A clever trick: Look for
with

22. How to find g0 and g1
To get g1, can we select probes with highest t-score?
Why or why not?

23. 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.

24. Step 3: control FDR Goal: To find null and signal distributions
Idea: assume a mixture model
This is unidentifiable!
A clever trick: Find
Additional assumption:
with

25. Step 3: control FDR Goal: To find null and signal distributions
Idea: assume a mixture model
This is unidentifiable!
A clever trick: Find
Additional assumption:
with

26. Step 3: Unbalanced mixture score
with
is estimated by fitting

27. False discovery rate (FDR)
Determine TF bindings sites are FDR cutoff

28. 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!

29. Example: Analysis of a cMyc binding data

30. Comparison of models

31. Simulation results

32. MAT Basic Idea: Baseline level correction
Standardize probe intensity with respect to the expected baseline value
(Johnson et al. 2006)

33. MAT
How to estimate the baseline values?

34. Estimated nucleotide effect

35. MAT
Standardization

36. (X.S. Liu)

37. Reading List Keles el 2004 Ji and Wong 2005 Johnson et al. 2006
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.