get_fit.expr: Calculating best fit of a power low on expression data
In GWENA: Pipeline for augmented co-expression analysis

Description Usage Arguments Details Value Examples

Computes correlation matrix of the gene expression data, adjust it depending of the type of network, then try to parameter a power law for best fit

get_fit.expr(
  data_expr,
  fit_cut_off = 0.9,
  cor_func = c("pearson", "spearman", "bicor", "other"),
  your_func = NULL,
  network_type = c("unsigned", "signed", "signed hybrid"),
  block_size = NULL,
  ...
)

`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 specifying 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]
`network_type`	string giving type of network to be used. Either "unsigned", "signed", "signed hybrid". See details.
`block_size`	integer giving size of blocks by which operations can be proceed. Helping if working with low capacity computers. If null, will be estimated.
`...`	any other parameter compatible with `pickSoftThreshold.fromSimilarity`

network_type indicate which transformation will be applied on the correlation matrix to return the similarity score.

signed: will modify the range [-1;1] to [0.5;1.5] (because of log10 beeing used for scale free index computation)
unsigned: will return absolute value (moving from [-1;1] to [0;1])
signed hybrid: will replace all negative values by 0 (moving from [-1;1] to [0;1])

A list containing power of the law for best fit, fit table, and metadata about the arguments used.