build_net {GWENA} | R Documentation

## Network building by co-expression score computation

### Description

Compute the adjacency matrix, then the TOM to build the network. Than detect the modules by hierarchical clustering and thresholding

### Usage

    build_net(
      data_expr,
      fit_cut_off = 0.9,
      cor_func = c("pearson", "spearman", "bicor", "other"),
      your_func = NULL,
      power_value = NULL,
      block_size = NULL,
      stop_if_no_fit = FALSE,
      network_type = c("unsigned", "signed", "signed hybrid"),
      tom_type = c("unsigned", "signed", "signed Nowick", "unsigned 2", "signed 2", "none"),
      keep_matrices = c("none", "cor", "adja", "both"),
      n_threads = NULL,
      ...
    )

### Arguments

`data_expr` | matrix or data.frame or SummarizedExperiment, expression data with genes as column and samples as row.
`fit_cut_off` | float, cut off by which R^2 (coefficient of determination) will be thresholded. Must be in ]0;1[.
`cor_func` | string, name of the correlation function to be used. Must be one of "pearson", "spearman", "bicor", "other". If "other", your_func must be provided
`your_func` | function returning correlation values. Final values must be in [-1;1]
`power_value` | integer, power to be applied to the adjacency matrix. If NULL, will be estimated by trying different power law fitting.
`block_size` | integer, size of blocks by which operations can be proceed. Helping if working with low capacity computers. If null, will be estimated.
`stop_if_no_fit` | boolean, does not finding a fit above fit_cut_off should stop process, or just print a warning and return the highest fitting power.
`network_type` | string, type of network to be used. Either "unsigned", "signed", "signed hybrid". See details.
`tom_type` | string, type of the topological overlap matrix to be computed. Either "none", "unsigned", "signed", "signed Nowick", "unsigned 2", "signed 2" and "signed Nowick 2". See detail at `TOMsimilarityFromExpr`.
`keep_matrices` | string, matrices to keep in final object. Can be one of "none", "cor", "adja", "both". It is usefull to keep both if you plant to use `compare_conditions`.
`n_threads` | integer, number of threads that can be used to paralellise the computing
`...` | any other parameter compatible with `adjacency.fromSimilarity`

### Value

list containing network matrix, metadata of input parameters and power fitting information.

### Examples

    net <- build_net(kuehne_expr[, seq_len(350)], n_threads = 1)

[Package GWENA version 1.0.1 Index]