nempi: Main function for NEM based perturbation imputation.

In cbg-ethz/nempi: Inferring unobserved perturbations from gene expression data

Main function for NEM based perturbation imputation.

Description

Infers perturbations profiles based on a sparse perturbation matrix and differential gene expression as log odds

Usage

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,
  ...
)

Arguments

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)

Value

nempi object

Author(s)

Martin Pirkl

Examples

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)

cbg-ethz/nempi documentation built on Nov. 9, 2023, 3:46 p.m.