Description Usage Arguments Value Author(s) Examples

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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 |

`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

1 2 3 4 5 6 7 8 | ```
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)
``` |

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