Differential Abundance Analysis of Label-Free Mass Spectrometry Data

abundances | Get the abundance matrix |

accessor_methods | Get different features and elements of the 'proDAFit' object |

as_replicate | Get numeric vector with the count of the replicate for each... |

cash-proDAFit-method | Fluent use of accessor methods |

coefficients | Get the coefficients |

coefficient_variance_matrices | Get the coefficients |

convergence | Get the convergence information |

distance_sq | Square distance between two Gaussian distributions |

dist_approx | Calculate an approximate distance for 'object' |

dist_approx_impl | Distance method for 'proDAFit' object |

feature_parameters | Get the feature parameters |

generate_synthetic_data | Generate a dataset according to the probabilistic dropout... |

grapes-zero_dom_mat_mult-grapes | Helper function that makes sure that NA * 0 = 0 in matrix... |

hyper_parameters | Get the hyper parameters |

invprobit | Inverse probit function |

invprobit_fast | Same thing as invprobit, but without the parameter validation |

median_normalization | Column wise median normalization of the data matrix |

mply_dbl | apply function that always returns a numeric matrix |

pd_lm | Fit a single linear probabilistic dropout model |

pd_lm.fit | The work horse for fitting the probabilistic dropout model |

pd_row_t_test | Row-wise tests of difference using the probabilistic dropout... |

predict-proDAFit-method | Predict the parameters or values of additional proteins |

proDA | Main function to fit the probabilistic dropout model |

proDAFit-class | proDA Class Definition |

proDA_package | proDA: Identify differentially abundant proteins in... |

reference_level | Get the reference level |

result_names | Get the result_names |

test_diff | Identify differentially abundant proteins |

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