nem: Implementation of the original NEM

View source: R/mnems.r

nemR Documentation

Implementation of the original NEM

Description

Infers a signalling pathway from perturbation experiments.

Usage

nem(
  D,
  search = "greedy",
  start = NULL,
  method = "llr",
  marginal = FALSE,
  parallel = NULL,
  reduce = FALSE,
  weights = NULL,
  runs = 1,
  verbose = FALSE,
  redSpace = NULL,
  trans.close = TRUE,
  subtopo = NULL,
  prior = NULL,
  ratio = TRUE,
  domean = TRUE,
  modulesize = 5,
  fpfn = c(0.1, 0.1),
  Rho = NULL,
  logtype = 2,
  modified = FALSE,
  tree = FALSE,
  learnRates = FALSE,
  stepSize = 0.01,
  ...
)

Arguments

D

data matrix with observed genes as rows and knock-down experiments as columns

search

either "greedy", "modules" or "exhaustive" (not recommended for more than five S-genes)

start

either NULL ("null") or a specific network to start the greedy

method

"llr" for log odds or p-values densities or "disc" for binary data

marginal

logical to compute the marginal likelihood (TRUE)

parallel

NULL for no parallel optimization or an integer for the number of threads

reduce

reduce search space (TRUE) for exhaustive search

weights

a numeric vector of weights for the columns of D

runs

the number of runs for the greedy search

verbose

for verbose output (TRUE)

redSpace

reduced search space for exhaustive search; see result of exhaustive search with reduce = TRUE

trans.close

if TRUE uses the transitive closure of adj

subtopo

optional matrix with the subtopology theta as adjacency matrix

prior

a prior network matrix for adj

ratio

if FALSE uses alternative distance for the model score

domean

if TRUE summarizes duplicate columns

modulesize

the max number of S-genes included in one module for search = "modules"

fpfn

numeric vector of length two with false positive and false negative rates

Rho

optional perturbation matrix

logtype

log base of the log odds

modified

if TRUE, assumes a preprocessed data matrix

tree

if TRUE forces tree; does not allow converging edges

learnRates

if TRUE learns rates for false positives/negatives

stepSize

numerical step size for learning rates

...

optional parameters for future search methods

Value

transitively closed matrix or graphNEL

Author(s)

Martin Pirkl

Examples

D <- matrix(rnorm(100*3), 100, 3)
colnames(D) <- 1:3
rownames(D) <- 1:100
adj <- diag(3)
colnames(adj) <- rownames(adj) <- 1:3
scoreAdj(D, adj)

cbg-ethz/mnem documentation built on June 29, 2024, 1:56 p.m.