similarity_matrix {mosbi} | R Documentation

## Compute similarities between biclusters

### Description

This function computes a similarity matrix between biclusters using different similarity metrics.

### Usage

    similarity_matrix(
      bics,
      MARGIN = "both",
      metric = 1L,
      prob_scale = FALSE,
      mat_row = 0L,
      mat_col = 0L,
      prl = FALSE
    )

### Arguments

`bics` | A list of bicluster objects.
`MARGIN` | Choose if the distance is computed over `"row"` , `"column"` or `"both"`.
`metric` | Integer indicating which metric is used. 1: Bray-Curtis similarity (default), 2: Jaccard index, 3: overlap coefficient, 4: Fowlkes–Mallows index.
`prob_scale` | Scale similarity by the probability of an overlap equal of higher to the observed one. The scaling is done by multiplying the similarity with `(1 - (1 / (1 - log(overlap_probability, base=100))))`. The probability is comupted using the function `p_overlap_2d_higher` for `MARGIN =="both"` and `p_overlap_higher` otherwise. Can be helpful for big imbalances of bicluster sizes.
`mat_row` | If `prob_scale == TRUE`, the number of rows of the input matrix for biclustering must be given.
`mat_col` | If `prob_scale == TRUE`, the number of columns of the input matrix for biclustering must be given.
`prl` | Compute the similarity matrix using multiple cores (works only for `MARGIN="both"`). The number of core can be defined by executing: `RcppParallel::setThreadOptions(numThreads = 4)` before running this function.

### Value

A numeric matrix of the similarities between all given biclusters.

### Examples

    b <- list(bicluster(row=c(1,2,3,4), column=c(1,2,3,4)),
            bicluster(row=c(3,4,5,6), column=c(3,4,5,6)))
    similarity_matrix(b)

[Package mosbi version 1.0.3 Index]