Description Usage Arguments Value Examples

View source: R/annotate_features.R

Estimates posterior probabilities of all possible theoretical features for each measured feature based on prior probabilities and additional information about adduct and isotope connections using a Gibbs sampler. This function is based on code of the internal function IPA.sampler.Add.Iso.Bio in the R package IPA (available on github at https://github.com/francescodc87/IPA).

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
compute_posterior_prob(
prior.prob,
dat,
add.m = NULL,
iso.m = NULL,
bio.m = NULL,
delta.add = 0.5,
delta.iso = 0.5,
delta.bio = 1,
ratio.tol = 0.8,
no.its = 1100,
burn = 100
)
``` |

`prior.prob` |
matrix with prior probabilities for each measured feature
in rows and theoretical features in columns (e.g. as generated by the
function |

`dat` |
data.frame with information about measured features with columns id (= unique identifier), mz (= measured mz value), intensity (= measured intensity). |

`add.m` |
matrix with information about adduct connections (e.g. as
generated by function |

`iso.m` |
matrix with information about isotope connections (e.g. as
generated by function |

`bio.m` |
matrix with information about biochemical connections (e.g. as
generated by function |

`delta.add` |
Numeric. Confidence on the information encoded in add.m (smaller value means higher confidence). |

`delta.iso` |
Numeric. Confidence on the information encoded in iso.m (smaller value means higher confidence). |

`delta.bio` |
Numeric. Confidence on the information encoded in bio.m (smaller value means higher confidence). |

`ratio.tol` |
Numeric. Minimum accepted ratio between thereoretical and observed intensity ratios between isotopes (default = 0.8). |

`no.its` |
Numeric. Number of iterations to be performed by the Gibbs sampler (default = 1100). |

`burn` |
Numeric. Number of initial iterations to be ignored when computing the posterior probabilities (default = 100). |

matrix with measured features in rows and theoretical possible features in columns. The cell i,j contains the posterior probability that measured feature i belongs to theoretical feature j.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
data("se.example")
data("info.features")
dat = prepare_data_for_annotation(se = se.example)
hits.m = find_hits(info.features = info.features,
dat = dat,
ppm = 20)
prior.prob = compute_prior_prob(hits.m = hits.m,
info.features = info.features,
dat = dat,
ppm = 20)
add.m = generate_connectivity_matrix(info.features = info.features,
type = "adducts")
set.seed(20200402)
post.prob = compute_posterior_prob(prior.prob = prior.prob,
dat = dat,
add.m = add.m,
delta.add = 0.1)
``` |

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