ND: Network Deconvolution
In luyiyun/Network-Deconvolution:

Description Usage Arguments Details Value Examples

View source: R/ND.R

Clean up a network adjacency matrix, filter the false-positive edges.

ND(
  mat,
  beta = 0.99,
  alpha = 1,
  control = FALSE,
  linear_mapping_before = TRUE,
  linear_mapping_after = TRUE
)

`mat`	Input matrix, if it is a square matrix, the program assumes Input matrix, if it is a square matrix, the program assumes between nodes i and j. Elements of matrix should be non-negative.
`beta`	Scaling parameter, the program maps the largest absolute eigenvalue Scaling parameter, the program maps the largest absolute eigenvalue between 0 and 1.
`alpha`	fraction of edges of the observed dependency matrix to be kept in deconvolution process.
`control`	If FALSE, displaying direct weights for observed interactions, if 1, displaying direct weights for both observed and non-observed interactions.
`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.

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.