DEtime: Inferring the perturbation time from biological time course...

In ManchesterBioinference/DEtime: inferring the perturbation time from biological time course data

Inferring the perturbation time from biological time course data

Description

This package implements the Gaussian regression framework for perturbation time point inferrence in a two sample case. The package contains two main functions: DEtime_infer and DEtime_rank. DEtime_infer is the main function for perturbation point inference and DEtime_rank is used to filter out these silent genes before any focused perturbation point inference work. The package works on the time course data from a wild-type and a perturbed system. Acting upon pre-defined testing perturbation time, the function goes over these perturbation time candidates and derives their likelihoods. From Bayes' theory, under a uniform prior assumption, the posterior distribution of the tested perturbation time is derived from their corresponding likeliooods. Maximum a posterior (MAP), mean or median of the posterior distribution can be taken as the solution to the estimated perturbation time point.

Examples

### Import simulated dataset
data(SimulatedData)

### Carrying out perturbation point inference for the first two genes in the
### data with filtering by a threshold of 45 for the loglikelihood ratio.
### This threshold is arbitrarily big and would not normally be used in practice.
### We adopt it here in order to reduce the running time of this example.

res_rank <- DEtime_rank(ControlTimes = ControlTimes, 
  ControlData = ControlData, PerturbedTimes = PerturbedTimes, 
  PerturbedData=PerturbedData, savefile=TRUE)

### Get the index of these data with loglikelihood ratio larger than 45
idx <- which(res_rank[,2]>45)

if (length(idx)>0){
    res <- DEtime_infer(ControlTimes = ControlTimes, 
      ControlData = ControlData[idx,], PerturbedTimes = PerturbedTimes, 
      PerturbedData = PerturbedData[idx,])
     ### Print a summary of the results
     print_DEtime(res)
     ### Plot the result of the gene with top loglikelihood ratio
     plot_DEtime(res,plot_gene_ID=as.character(idx))
 }

ManchesterBioinference/DEtime documentation built on Feb. 9, 2024, 12:10 p.m.