nempi {nempi} | R Documentation |
Infers perturbations profiles based on a sparse perturbation matrix and differential gene expression as log odds
nempi( D, unknown = "", Gamma = NULL, type = "null", full = TRUE, verbose = FALSE, logtype = 2, null = TRUE, soft = TRUE, combi = 1, converged = 0.1, complete = TRUE, mw = NULL, max_iter = 100, keepphi = TRUE, start = NULL, phi = NULL, ... )
D |
either a binary effects matrix or log odds matrix as for Nested Effects Models (see package 'nem') |
unknown |
colname of samples without mutation data, E.g. "" |
Gamma |
matrix with expectations of perturbations, e.g. if you have a binary mutation matrix, just normalize the columns to have sum 1 |
type |
"null": does not use the unknown samples for inference at the start, "random" uses them in a random fashion (not recommended) |
full |
if FALSE, does not change the known profiles |
verbose |
if TRUE gives more output during inference |
logtype |
log type for the log odds |
null |
if FALSE does not use a NULL node for uninformative samples |
soft |
if FALSE discretizes Gamma during the inference |
combi |
if combi > 1, uses a more complex algorithm to infer combinatorial perturbations (experimental) |
converged |
the absolute difference of log likelihood till convergence |
complete |
if TRUE uses the complete-data logliklihood (recommended for many E-genes) |
mw |
if NULL infers mixture weights, otherwise keeps them fixed |
max_iter |
maximum iterations of the EM algorithm |
keepphi |
if TRUE, uses the previous phi for the next inference, if FALSE always starts with start network (and empty and full) |
start |
starting network as adjacency matrix |
phi |
if not NULL uses only this phi and does not infer a new one |
... |
additional parameters for the nem function (see package mnem, function nem or mnem::nem) |
nempi object
Martin Pirkl
D <- matrix(rnorm(1000*100), 1000, 100) colnames(D) <- sample(seq_len(5), 100, replace = TRUE) Gamma <- matrix(sample(c(0,1), 5*100, replace = TRUE, p = c(0.9, 0.1)), 5, 100) Gamma <- apply(Gamma, 2, function(x) return(x/sum(x))) Gamma[is.na(Gamma)] <- 0 rownames(Gamma) <- seq_len(5) result <- nempi(D, Gamma = Gamma)