Man pages for proDA
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 |
