Description Usage Arguments Details Value Author(s) See Also Examples

This function calculates the product of the standardized differences between two conditions in ChIP-seq data and the respective standardized differences in gene expression data. A score close to zero means that there are no (large) differences in at least one of the two data sets. If the score is positive, equally directed differences exist in both data sets. In case of a negative score, differences have unequal signs in the two data sets.

1 | ```
integrateData(expr, chipseq, factor, reference)
``` |

`expr` |
An |

`chipseq` |
A |

`factor` |
A |

`reference` |
Optionally, the name of the factor level that should be used as
reference. If missing, the first level of |

Let A and B denote the gene expression value of one probe in the group
of interest and in the reference group defined by the argument
`reference`

. And let X and Y be the ChIP-seq values assigned to
that probe. This functions returnes for each probe

*Z = (A-B)/σ_{ge} \times (X-Y)/σ_{chip},*

where *σ_{ge}* is the standard deviation estimated from all
observed difference in the gene expression data and *σ_{chip}*
the standard deviation in the ChIP-seq data.

If there is more than one sample in any group and data set, the average of the replicates is calcuated first and than plugged into the formula above.

Not all features in `expr`

must also be in `chipseq`

and
vice versa. Features present in only one of the two data types are
omitted.

A matrix with five columns. The first 4 columns store the (average)
expression values and the (average) ChIP-seq values for each of the two
conditions. The fith columns store the correlation score. The row names
equal common feature names of `expr`

and `chipseq`

.

Hans-Ulrich Klein ([email protected])

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
ge <- matrix(c(5,12,5,11,11,10,12,11), nrow=2)
row.names(ge) <- c("100_at", "200_at")
colnames(ge) <- c("c1", "c2", "t1", "t2")
geDf <- data.frame(status=c("control", "control", "treated", "treated"),
row.names=colnames(ge))
eSet <- ExpressionSet(ge, phenoData=AnnotatedDataFrame(geDf))
chip <- matrix(c(10,20,20,22), nrow=2)
row.names(chip) <- c("100_at", "200_at")
colnames(chip) <- c("c", "t")
rowRanges <- GRanges(IRanges(start=c(10,50), end=c(20,60)), seqnames=c("1","1"))
names(rowRanges) = c("100_at", "200_at")
chipDf <- DataFrame(status=factor(c("control", "treated")),
totalCount=c(100, 100),
row.names=colnames(chip))
cSet <- ChIPseqSet(chipVals=chip, rowRanges=rowRanges, colData=chipDf)
integrateData(eSet, cSet, factor="status", reference="control")
``` |

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