Rediscover (version 0.3.3)

getMutex: getMutex function

Description

Given a binary matrix and its corresponding probability matrix pij, compute the Poisson Binomial method to estimate mutual exclusive events.

Usage

getMutex(
  A = NULL,
  PM = getPM(A),
  lower.tail = TRUE,
  method = "ShiftedBinomial",
  mixed = TRUE,
  th = 0.05,
  verbose = FALSE,
  parallel = FALSE,
  no_cores = NULL
)

Value

A symmetric matrix with the p-values of the corresponding test.

Arguments

A

The binary matrix

PM

The corresponding probability matrix of A. It can be computed using function getPM. By default equal to getPM(A)

lower.tail

True if mutually exclusive test. False for co-ocurrence. By default is TRUE.

method

one of the following: "ShiftedBinomial" (default),"Exact", "Binomial", and "RefinedNormal".

mixed

option to compute lower p-values with an exact method. By default TRUE

th

upper threshold of p.value to apply the exact method.

verbose

The verbosity of the output

parallel

If the exact method is executed with a parallel process.

no_cores

number of cores. If not stated number of cores of the CPU - 1

Details

we implemented three different approximations of the Poison-Binomial distribution function:

• "ShiftedBinomial" (by default) that correspond to a shifted Binomial with three parameters (Peköz, Shwartz, Christiansen, & Berlowitz, 2010).

• "Exact" that use the exact formula using the `PoissonBinomial` Rpackage based on the work from (Biscarri, Zhao, & Brunner, 2018).

• "Binomial" with two parameters (Cam, 1960).

• "RefinedNormal" that is based on the work from (Volkova, 1996).

If `mixed` option is selected (by default is FALSE), the "Exact" method is computed for p-values lower than a threshold (`th` parameter, that by default is 0.05). When the exact method is computed, it is possible to parallelize the process by selecting the option `parallel` (by default FALSE) and setting the number of cores (`no_cores` parameter)

Examples

  #This first example is a basic 
  #example of how to perform getMutex. 
  
  data("A_example")
  PMA <- getPM(A_example)
  mismutex <- getMutex(A=A_example,PM=PMA)
  
  # \donttest{
  #The next example, is the same as the first one but,
  # using a matrix of class Matrix. 
  
  data("A_Matrix")
  A_Matrix <- A_Matrix[1:100,1:50]
  #small for the example
  PMA_Matrix <- getPM(A_Matrix)
  mismutex <- getMutex(A=A_Matrix,PM=PMA_Matrix)
  
  
  if (FALSE) {
  #Finally, the last example, shows a real 
  #example of how to perform this function when using 
  #data from TCGA, Colon Adenocarcinoma in this case. 
  
  data("TCGA_COAD")
  data("PM_COAD")
  
  PM_COAD <- getMutex(TCGA_COAD, PM_COAD)
  }
  # }