Description Usage Arguments Value See Also Examples

This function takes a `GInteractions`

object with
candidate looping interactions. It should be annotated with features in
metadata columns. A logistic regression model is applied to predict looping
interaction probabilities.

1 2 |

`gi` |
A |

`formula` |
A |

`betas` |
A vector with parameter estimates for predictor variables. They
should be in the same order as variables in |

`colname` |
A |

`cutoff` |
Numeric cutoff on prediction score. Only interactions with
interaction probability >= |

A `GInteractions`

as `gi`

with an
additional metadata column holding the predicted looping probability.

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 27 28 29 30 | ```
# use example CTCF moitf location on human chromosome 22 with chip coverage
motifGR <- sevenC::motif.hg19.CTCF.chr22.cov
# build candidate interactions
gi <- prepareCisPairs(motifGR)
# add ChIP-seq signals correlation
gi <- addCovCor(gi)
# predict chromatin looping interactions
loops <- predLoops(gi)
# add prediction score for all candidates without filter
gi <- predLoops(gi, cutof = NULL)
# add prediction score using custom column name
gi <- predLoops(gi, cutof = NULL, colname = "my_colname")
# Filter loop predictions on custom cutoff
loops <- predLoops(gi, cutoff = 0.4)
# predict chromatin looping interactions using custom model parameters
myParams <- c(-4, -5, -2, -1, -1, 5, 3)
loops <- predLoops(gi, betas = myParams)
# predict chromatin loops using custom model formula and params
myFormula <- ~ dist + score_min
# define parameters for intercept, dist and motif_min
myParams <- c(-5, -4, 6)
loops <- predLoops(gi, formula = myFormula, betas = myParams)
``` |

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