Scans multi-platform data for all variable length windows have projected chi-square statistc higher than a fixed user-set threshold.

1 | ```
mpscan(y, pos, anchors, win)
``` |

`y` |
A vector of intensity levels for each platform. y[[k]] should be the intensity levels corresponding to pos[[k]] for platform k. |

`pos` |
A vector of sorted integer arrays, one array for each platform. pos[[k]] should give the positions, in increasing order, of the probes of the k-th platform. |

`anchor` |
The anchor set, returned by a call to merge.pos(...) |

`win` |
Largest allowed window size. |

`MIN.SNPs` |
Minimum number of SNPs allowed in a region. |

`WCHISQ.CUTOFF` |
Threshold level for the weighted chi-square statistic. |

`yhat` |
A vector of fitted y values, yhat[[k]] contains the fitted values to y[[k]]. |

`chpts` |
A list of change-points, there are shared across platforms. |

`platform.chisq` |
The contribution of each platform to the overall chi-square value for each interval. |

`Z` |
The Z matrix. |

Nancy R. Zhang

Zhang, NR, Senbabaoglu, Y. and Li, J.Z. (2009) Joint Estimation of DNA Copy Number from Multiple Platforms. Under review, download manuscript from http://www-stat.stanford.edu/~nzhang/web_multiplatform/

mpcbs, plot.crossplatform, merge.pos

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | ```
data(mpcbs.example)
# there are 3 platforms represented in this data example: Illumina, Affymetrix, and Agilent.
names(mpcbs.example)
# K is the number of platforms.
K=3
# Store the chromosome positions in vector pos,
# the intensities in vector y:
pos=vector("list",K)
pos[[1]] = mpcbs.example$illu[,1]
pos[[2]] = mpcbs.example$affy[,1]
pos[[3]] = mpcbs.example$agil[,1]
y = vector("list",K)
y[[1]] = mpcbs.example$illu[,2]
y[[2]] = mpcbs.example$affy[,2]
y[[3]] = mpcbs.example$agil[,2]
# Names of the platforms:
platform.names=c("Illumina","Affymetrix","Agilent")
# Get the anchor set.
anchor = merge.pos(pos)
``` |

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