get_fit.expr: Calculating best fit of a power low on expression data

In Kumquatum/GWENA: Pipeline for augmented co-expression analysis

Calculating best fit of a power low on expression data

Description

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

Usage

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,
  ...
)

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 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

Details

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])

Value

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

Examples

get_fit.expr(kuehne_expr[, seq_len(100)])

Kumquatum/GWENA documentation built on July 7, 2023, 3:41 p.m.