classpi: Classification

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

Classification

Description

Builds and uses different classifiers to infer perturbation profiles

Usage

classpi(
  D,
  unknown = "",
  full = TRUE,
  method = "svm",
  size = NULL,
  MaxNWts = 10000,
  ...
)

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. ""
full	if FALSE, does not change the known profiles
method	either one of svm, nn, rf
size	parameter for neural network (see package 'nnet')
MaxNWts	parameters for neural network (see package 'nnet')
...	additional parameters for mnem::nem

Value

plot

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 <- classpi(D)

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