predLoops {sevenC} | R Documentation

## Predict looping interactions.

### Description

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.

### Usage

    predLoops(gi, formula = NULL, betas = NULL, colname = "pred",
      cutoff = get("cutoffBest10"))

### Arguments

`gi` | A `GInteractions` object with coverage correlation and genomic features in metadata columns. See prepareCisPairs and addCor to build it.
`formula` | A `formula`. All predictor variables should be available in the in metadata columns of `gi`. If NULL, the following default formula is used: `~ dist + strandOrientation + score_min + chip`.
`betas` | A vector with parameter estimates for predictor variables. They should be in the same order as variables in `formula`. Per default estimates of `modelBest10Avg` are used. See `?modelBest10Avg` for more detailed information on each parameter.
`colname` | A `character` as column name of new metadata column in `gi` for predictions.
`cutoff` | Numeric cutoff on prediction score. Only interactions with interaction probability >= `cutoff` are reported. If `NULL`, all input interactions are reported. Default is `cutoffBest10`, an optimal cutoff based on F1-score on 10 best performing transcription factor ChIP-seq data sets. See `?cutoffBest10` for more details.

### Value

A `GInteractions` as `gi` with an additional metadata column holding the predicted looping probability.

### See Also

`prepareCisPairs`, `addCor`

### Examples

    # 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)

[Package sevenC version 1.2.0 Index]