Function reference • protDP

Fit an empirical logistic spline

logitSplines_start(): Get the initial estimates for a logistic spline fit by assuming the binomial detection probability

logit_ztbinom(): Fitting an empirical logistic spline curve for detection proportion

cappedLogit_ztbinom(): Fitting an empirical logistic spline curve for detection proportion with capped probabilities

Fit a detection probability curve

dpc(): Detection probability curve for label free shotgun proteomics data assuming observed normal intensities

Plot a fitted curve

plotEmpSplines(): Plot the fitted empirical spline of detected proportions to average observed intensities

plotDPC(): Plot the detection probability curve

Supporting functions

getNuisance(): Get nuisance parameters and an initial estimation of the detection probability curve (DPC)

hyperparams(): Estimation of hyperparameters for the empirical Bayes method

gatherResults(): Wrapper function of all presented results

Datasets

datasetA: Dataset A: Hybrid proteome data

datasetB: Dataset B: Single cell proteomes

datasetC: Dataset C: HepG2 technical replicate data

datasetD: Dataset D: Human blood plasma proteome

shbheart: Supplementary Dataset: Sydney Heart Bank data

ratio2.5: Supplementary Dataset : UPS1 spiked in yeast extract