nem {mnem} | R Documentation |
Infers a signalling pathway from peerturbation experiments.
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, ... )
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 prepocessed data matrix |
... |
optional parameters for future search methods |
transitively closed matrix or graphNEL
Martin Pirkl
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)