R: Differential Abundance Analysis of Label-Free Mass Spectrometry Data

# Differential Abundance Analysis of Label-Free Mass Spectrometry Data

## Documentation for package ‘proDA’ version 1.27.0

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

$-method | Fluent use of accessor methods
$<--method | Fluent use of accessor methods
.DollarNames.proDAFit | Fluent use of accessor methods
.proDAFit | proDA Class Definition
abundances | Get the abundance matrix
abundances-method | Get different features and elements of the 'proDAFit' object
accessor_methods | Get different features and elements of the 'proDAFit' object
coefficients | Get the coefficients
coefficients-method | Get different features and elements of the 'proDAFit' object
coefficient_variance_matrices | Get the coefficients
coefficient_variance_matrices-method | Get different features and elements of the 'proDAFit' object
convergence | Get the convergence information
convergence-method | Get different features and elements of the 'proDAFit' object
design-method | Get different features and elements of the 'proDAFit' object
dist_approx | Calculate an approximate distance for 'object'
dist_approx-method | Distance method for 'proDAFit' object
dist_approx_impl | Distance method for 'proDAFit' object
dollar_methods | Fluent use of accessor methods
feature_parameters | Get the feature parameters
feature_parameters-method | Get different features and elements of the 'proDAFit' object
generate_synthetic_data | Generate a dataset according to the probabilistic dropout model
hyper_parameters | Get the hyper parameters
hyper_parameters-method | Get different features and elements of the 'proDAFit' object
invprobit | Inverse probit function
median_normalization | Column wise median normalization of the data matrix
pd_lm | Fit a single linear probabilistic dropout model
pd_row_f_test | Row-wise tests of difference using the probabilistic dropout model
pd_row_t_test | Row-wise tests of difference using the probabilistic dropout model
predict-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 label-free mass spectrometry
reference_level | Get the reference level
reference_level-method | Get different features and elements of the 'proDAFit' object
result_names | Get the result_names
result_names-method | Identify differentially abundant proteins
test_diff | Identify differentially abundant proteins