DEtime_infer | R Documentation |
This is the main function in DEtime Package, which applies a mixedGP kernel to time course data under control and perturbed conditions. It returns the posterior distribution of these predefined perturbation time candidates and relevant statistical estimations of the inferred perturbation time point.
DEtime_infer(ControlTimes, ControlData, PerturbedTimes, PerturbedData,
TestTimes = NULL, gene_ID = NULL, bound.lengthscale = NULL)
ControlTimes |
Experimental time point at which time course biological data for the control case are measured, they have to be repeated if there are replicated measurements |
ControlData |
Time course data measured under control condition |
PerturbedTimes |
Experimental time point at which time course biological data for the perturbed case are measured, they have to be repeated if there are replicated measurements |
PerturbedData |
Time course data measured under perturbed condition |
TestTimes |
The predefined evenly spaced time points upon which perturbation will be evaluated. If undefined, TestTimes <- seq(min(c(ControlTimes, PerturbedTimes)), max(c(ControlTimes, PerturbedTimes)),length=50) |
gene_ID |
ID of these genes addressed in this study. If undefinied, numbers will be used instead |
bound.lengthscale |
bounds for the lengthscale used in the DEtime RBF kernel. When not provided,bound.lengthscale <- c(min(ControlTimes,PerturbedTimes), 4*max(c(ControlTimes,PerturbedTimes))) |
ControlTimes and PerturbedTimes can be ordered by either time series, for instance time1, time1, time2, time2, time3, time3 ... or replicate sequences, for instance: time1, time2, time3, time1, time2, time3. ControlData and PerturbedData are two matrices where each row represents the time course data for one particular gene under either control or perturbed condition. The orders of the ControlData and PeruturbedData have to match those of the ControlTimes and PerturbedTimes, respectively.
The function will return a DEtimeOutput object which includes:
result: the statistical estimation for the inferred perturbation time
____$MAP: maximum a posterior solution to the inferred perturbation time
____$mean: mean of the posterior distribution of the inferred perturbation time
____$median: median of the posterior distribution of the inferred perturbation time
____$ptl5: 5 percentile of the posterior distribution of the inferred perturbation time
_____$ptl95: 95 percentile of the posterior distribution of the inferred perturbation time
posterior: posterior distribution of the tested perturbation time points
model: optimized GP model which will be used for later GP regression work
best_param: optimized hyperparameter for the optimized GP model
ControlTimes: original experimental time points for the control case which will be used for future print or plot functions
ControlData: original measured time course data for the control case which will be used for future print or plot functions
PerturbedTimes: original experimental time points for the perturbed case which will be used for future print or plot functions
PerturbedData: original measured time course data for the control case which will be used for future print or plot functions
TestTimes: tested perturbation time points
gene_ID: the ID of genes for the data
### import simulated data
data(SimulatedData)
### start perturbation time inference
res <- DEtime_infer(ControlTimes = ControlTimes, ControlData = ControlData,
PerturbedTimes = PerturbedTimes, PerturbedData = PerturbedData)
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