Man pages for const-ae/proDA
Differential Abundance Analysis of Label-Free Mass Spectrometry Data

abundancesGet the abundance matrix
accessor_methodsGet different features and elements of the 'proDAFit' object
as_replicateGet numeric vector with the count of the replicate for each...
cash-proDAFit-methodFluent use of accessor methods
coefficientsGet the coefficients
coefficient_variance_matricesGet the coefficients
convergenceGet the convergence information
distance_sqSquare distance between two Gaussian distributions
dist_approxCalculate an approximate distance for 'object'
dist_approx_implDistance method for 'proDAFit' object
feature_parametersGet the feature parameters
generate_synthetic_dataGenerate a dataset according to the probabilistic dropout...
grapes-zero_dom_mat_mult-grapesHelper function that makes sure that NA * 0 = 0 in matrix...
hyper_parametersGet the hyper parameters
invprobitInverse probit function
invprobit_fastSame thing as invprobit, but without the parameter validation
median_normalizationColumn wise median normalization of the data matrix
mply_dblapply function that always returns a numeric matrix
pd_lmFit a single linear probabilistic dropout model
pd_lm.fitThe work horse for fitting the probabilistic dropout model
pd_row_t_testRow-wise tests of difference using the probabilistic dropout...
predict-proDAFit-methodPredict the parameters or values of additional proteins
proDAMain function to fit the probabilistic dropout model
proDAFit-classproDA Class Definition
proDA_packageproDA: Identify differentially abundant proteins in...
reference_levelGet the reference level
result_namesGet the result_names
test_diffIdentify differentially abundant proteins
const-ae/proDA documentation built on July 23, 2020, 9:04 p.m.