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**Chromatin Immuno-precipitation (CHIP)-chip Analysis**

11/07/07

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**Experimental Protocol**

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

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**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

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**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

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**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

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

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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 CHIP-signal Y2 Y1**

Input-signal i Keles et al. 2004

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**Keles’ method Calculate a two-sample t-statistic**

CHIP-signal Y2 Y1 Moving average scan-statistic Input-signal i

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**Multiple hypothesis testing**

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

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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.

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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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**Step 1: calculating a t-like test statistic**

Two samples: Multiple samples: Want to have a robust estimate of variance.

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**Step 1: calculating a t-like test statistic**

Estimation of by variance shrinkage Shrinkage factor Notation

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**Step 2: Merging data Moving average**

Alternatively use Hidden Markov Model

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**Step 3: control FDR Goal: To find null and signal distributions**

Idea: assume a mixture model This is unidentifiable!

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**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

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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.

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**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

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**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

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**Step 3: Unbalanced mixture score**

with is estimated by fitting

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**False discovery rate (FDR)**

Determine TF bindings sites are FDR cutoff

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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. Memory problem!

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**Example: Analysis of a cMyc binding data**

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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**

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MAT Standardization

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(X.S. Liu)

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**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.

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