bicluster_network {mosbi} | R Documentation

## Generate a bicluster network

### Description

The function computes a bicluster network based on a selected similarity metric. A similarity cut-off is calculated using randomized biclusters (the bicluster size distribution is kept).

### Usage

    bicluster_network(
      bics,
      mat,
      n_randomizations = 5,
      MARGIN = "both",
      metric = 4,
      n_steps = 100,
      plot_edge_dist = TRUE,
      sn_ratio = TRUE,
      error_threshold = 0.05,
      return_plot_data = FALSE,
      prob_scale = FALSE,
      prl = FALSE
    )

### Arguments

`bics` | A list of bicluster objects.
`mat` | The matrix used for biclustering.
`n_randomizations` | The number of randomizations for cut-off estimation. (The mean of all randomizations is used).
`MARGIN` | Margin over which the similarity is computed. Can be "row", "column", "mean" (In this case the mean of row and column similarity is used) or "both" (In this case the similarity between all the datapoints of biclusters is used).
`metric` | The similarity metric same as in `similarity_matrix`.
`n_steps` | Number of points where the difference between randomizations and the real data is evaluated.
`plot_edge_dist` | Show the plots for cut-off estimation with the error model.
`sn_ratio` | If `TRUE`, the signal to noise ratio is computed, otherwise the `error_threshold` is used to to estimate the cut-off at which only `error_threshold*100` percent of the edges are estimated to be random overlaps.
`error_threshold` | If `sn_ratio==FALSE` this threshold is used to estimate the threshold at which only `error_threshold*100` percent of the edges are estimated to be random overlaps.
`return_plot_data` | Please do not use outside of the package.
`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 computed using the f unction `p_overlap_2d_higher` for `MARGIN =="both"` and `p_overlap_higher` otherwise. Can be helpful for big imbalances of bicluster sizes.
`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

An object of class `bicluster_net`.

### Examples

    m <- matrix(seq(1:16), nrow=4)
    # m <- matrix(rnorm(10000), nrow=100)
    # bics <- c(run_fabia(m), run_isa(m), run_plaid(m))
    # bicluster_network(bics, m)

[Package mosbi version 1.0.3 Index]