ND_regulatory: Network deconvolution for gene regulatory networks of DREAM5
In luyiyun/Network-Deconvolution:

Description Usage Arguments Details Value Examples

View source: R/ND_regulatory.R

This function can suit for non-square matrix

ND_regulatory(
  mat,
  beta = 0.5,
  alpha = 0.1,
  linear_mapping_before = TRUE,
  linear_mapping_after = TRUE,
  control_p = FALSE
)

`mat`	Input matrix, it is a n_tf by n matrix where first n_tf genes are TFs. Elements of the input matrix are nonnegative.
`beta`	Scaling parameter, the program maps the largest absolute eigenvalue of the direct dependency matrix to beta. It should be between 0 and 1. You should skip this scaling step if you know eigenvalues of your matrix satisfy ND conditions.
`alpha`	fraction of edges of the observed dependency matrix to be kept in deconvolution process.
`linear_mapping_before`	If TRUE, mat will be linearly mapped to be between 0 and 1 before deconvolution.
`linear_mapping_after`	If TRUE, result will be linearly mapped to be between 0 and 1 after deconvolution.
`control_p`	If set to TRUE, it perturbs input networks slightly to have stable results in case of non-diagonalizable matrices. If FALSE, it checks some sufficient condition and then add a small perturbation (this may be slower). Default is FALSE.

Feizi, S.; Marbach, D.; Médard, M.; Kellis, M., Network deconvolution as a general method to distinguish direct dependencies in networks. Nature Biotechnology 2013, 31, 726–733.

mat_nd, Output deconvolved matrix (direct dependency matrix). Its components represent direct edge weights of observed interactions. Choosing top direct interactions (a cut-off) depends on the application and is not implemented in this code.