# BAC: Bayesian Analysis of ChIP-chip tiling arrays In Bioconductor-mirror/BAC: Bayesian Analysis of Chip-chip experiment

## Description

Bayesian Analysis of ChIP-chip tiling arrays

## Usage

 `1` ```BAC(C, I, B=15000,verbose=FALSE,w=5) ```

## Arguments

 `C` The matrix of control measurements. Rows correspond to probes and columns to samples. `I` The matrix of IP measurements. Rows correspond to probes and columns to samples. `B` Number of iterations used the MCMC. Default to 15000. `verbose` Logical parameter. If TRUE, some progression `w` The window size. Default to 5. See details below for more about this parameter.

## Details

The window size should be calculated in function of the resolution and the shearing resolution. For example, for Affymetrix human tiling arrays, the shearing resolution is 500-1000bps, the tiling resolution is 35bps and the probe length is 25bps. Then one would expect a bound region to contain 500-1000/(35+25)~8-16 probes. Thus we decided to set w to 5. Note that the exact value of w is not crucial.

## Value

The marginal posterior probabilities and the joint posterior probabilities computed from the Bayesian hierarchical model. We recommend using the joint posterior probabilities to call enriched regions.

## Author(s)

Raphael Gottardo, [email protected]

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24``` ```# Load the data data(ER) # Only select the first 5000 probes for speed-up ER<-ER[1:5000,] # Calculate the joint posterior probabilities #Only use 100 iterations for speed up (You should use more! See default value) BAConER<-BAC(ER[,5:7], ER[,2:4], B=100,verbose=FALSE,w=5) # For Regions using 0.5 cut-off for the joint posterior probabilities ERregions<-CallRegions(ER[,1],BAConER\$jointPP,cutoff=0.5,maxGap=500) # Create the BED file nRegions<-max(ERregions) BED<-matrix(0,nRegions,4) for(i in 1:nRegions) { BED[i,2:3]<-range(ER[ERregions==i,1]) #The score should be between 0 and 1000 BED[i,4]<-max(BAConER\$jointPP[ERregions==i])*1000 } BED<-data.frame(BED) # The ER data is a subset of chr 21 BED[,1]<-"chr21" names(BED)<-c("chrom","chromStart","chromEnd","Score") # print it print(BED) ```