AMP_COAD data

Description

A binary matrix, with information about amplifications in Colon Adenocarcinoma, created by applying GDCquery and used as real example in getMutexAB.

Usage

data("AMP_COAD")

Format

The format is:

num [1:1000, 1:391] 0 0 0 0 0 0 0 0 0 0 ...

- attr(*, "dimnames")=List of 2

..$ : chr [1:1000] "ENSG00000212993.4" "ENSG00000279524.1" "ENSG00000136997.13" "ENSG00000101294.15" ...

..$ : chr [1:391] "TCGA-CA-6718" "TCGA-D5-6931" "TCGA-AZ-6601" "TCGA-G4-6320" ...

Examples

data(AMP_COAD)
## maybe str(AMP_COAD) 

A_Matrix data

Description

A binary dgCMatrix matrix used as toy example in getPM and getMutex and getMutexAB and getMutexGroup

Usage

data("A_Matrix")

Format

The format is:

Formal class 'dgCMatrix' [package "Matrix"] with 6 slots

..@ i : int [1:249838] 5 9 10 11 13 14 18 20 23 24 ...

..@ p : int [1:501] 0 503 1010 1506 1995 2497 2981 3488 4002 4474 ...

..@ Dim : int [1:2] 1000 500

..@ Dimnames:List of 2

.. ..$ : NULL

.. ..$ : NULL

..@ x : num [1:249838] 1 1 1 1 1 1 1 1 1 1 ...

..@ factors : list()

Examples

data(A_Matrix)

A_example data

Description

A binary matrix of class matrix used as toy example in getPM and getMutex and getMutexAB and getMutexGroup.

Usage

data("A_example")

Format

The format is: num [1:1000, 1:500] 0 0 0 0 0 1 0 0 0 1 ...

Examples

data(A_example)

B_Matrix data

Description

A binary dgCMatrix matrix used as toy example in getPM and getMutex and getMutexAB and getMutexGroup.

Usage

data("B_Matrix")

Format

The format is:

Formal class 'dgCMatrix' [package "Matrix"] with 6 slots

..@ i : int [1:249526] 1 2 4 8 9 11 13 15 18 20 ...

..@ p : int [1:501] 0 498 1014 1527 2048 2558 3036 3511 4035 4537 ...

..@ Dim : int [1:2] 1000 500

..@ Dimnames:List of 2

.. ..$ : NULL

.. ..$ : NULL

..@ x : num [1:249526] 1 1 1 1 1 1 1 1 1 1 ...

..@ factors : list()

Examples

data(B_Matrix)

B_example data

Description

A binary matrix of class matrix used as toy example in getPM and getMutex and getMutexAB and getMutexGroup.

Usage

data("B_example")

Format

The format is:

int [1:1000, 1:500] 0 1 1 0 1 0 0 0 1 1 ...

Examples

data(B_example)

Rediscover Internal Functions

Description

Internal functions used by EventPointer in the different steps of the algorithm

Usage

speedglm.wfit2(
  y,
  X,
  intercept = TRUE,
  weights = NULL,
  row.chunk = NULL,
  family = gaussian(),
  start = NULL,
  etastart = NULL,
  mustart = NULL,
  offset = NULL,
  acc = 1e-08,
  maxit = 25,
  k = 2,
  sparselim = 0.9,
  camp = 0.01,
  eigendec = TRUE,
  tol.values = 1e-07,
  tol.vectors = 1e-07,
  tol.solve = .Machine$double.eps,
  sparse = NULL,
  method = c("eigen", "Cholesky", "qr"),
  trace = FALSE,
  ...
)

expand.grid_fast(a, b)

get_os()

control_2(
  B,
  symmetric = TRUE,
  tol.values = 1e-07,
  tol.vectors = 1e-07,
  out.B = TRUE,
  method = c("eigen", "Cholesky")
)

is.sparse_2(X, sparselim = 0.9, camp = 0.05)

Value

Internal outputs

PM_AMP_COAD data

Description

Probability matrix, with information of genes being amplified in samples in Colon Adenocarcinoma, created by AMP_COAD.rda applying getPM and used as real example and getMutexAB.

Usage

data("PM_AMP_COAD")

Format

The format is:

num [1:1000, 1:391] 0.118 0.118 0.118 0.118 0.114 ...

Examples

data(PM_AMP_COAD)

PM_COAD data

Description

Probability matrix, with information of genes being mutated in samples in Colon Adenocarcinoma, created by TCGA_COAD.rda applying getPM and used as real example in getMutex and getMutexAB and getMutexGroup.

Usage

data("PM_COAD")

Format

The format is:

Formal class 'PMatrix' [package "Rediscover"] with 2 slots

..@ rowExps: num [1:399] 13.1 1.02 7.43 3.26 0.4 ...

..@ colExps: num [1:17616] 2.54 1.78 1.76 1.35 0.6 ...

Examples

data(PM_COAD)

An S4 class to store the probabilities

Description

An S4 class to store the probabilities of gene i being mutated in sample j

Slots

rowExps

Sample depending estimated coefficients obtained from the logistic regression

colExps

gene depending estimated coefficients obtained from the logistic regression

TCGA_COAD

Description

A binary matrix, with information about genes mutations in Colon Adenocarcinoma, created by applying maftools to .maf file and used as real example in getPM and getMutex and getMutexAB and getMutexGroup.

Usage

data("TCGA_COAD")

Format

The format is:

num [1:399, 1:17616] 1 1 1 1 1 1 1 1 1 1 ...

- attr(*, "dimnames")=List of 2

..$ : chr [1:399] "TCGA-CA-6718" "TCGA-D5-6931" "TCGA-AZ-6601" "TCGA-G4-6320" ...

..$ : chr [1:17616] "APC" "TP53" "TTN" "KRAS" ...

Examples

data(TCGA_COAD)

discoversomaticInteractions

Description

Function adapted to maftools where given a .maf file, it graphs the somatic interactions between a group of genes, i.e., the combination of gene expression and mutation data to detect mutually exclusive or co-ocurring events.

Usage

discoversomaticInteractions(
  maf,
  top = 25,
  genes = NULL,
  pvalue = c(0.05, 0.01),
  getMutexMethod = "ShiftedBinomial",
  getMutexMixed = TRUE,
  returnAll = TRUE,
  geneOrder = NULL,
  fontSize = 0.8,
  showSigSymbols = TRUE,
  showCounts = FALSE,
  countStats = "all",
  countType = "all",
  countsFontSize = 0.8,
  countsFontColor = "black",
  colPal = "BrBG",
  showSum = TRUE,
  colNC = 9,
  nShiftSymbols = 5,
  sigSymbolsSize = 2,
  sigSymbolsFontSize = 0.9,
  pvSymbols = c(46, 42),
  limitColorBreaks = TRUE
)

Arguments

Details

Internally, this function run the getMutex function. With the 'getMutexMethod' parameter user might select the 'method' parameter of the getMutex function. For more details run '?getMutex'

#' @return A list of data.tables and it will print a heatmap with the results.

References

Mayakonda A, Lin DC, Assenov Y, Plass C, Koeffler HP. 2018. Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Research. http://dx.doi.org/10.1101/gr.239244.118

Examples

## Not run: 

  #An example of how to perform the function,
  #using data from TCGA, Colon Adenocarcinoma in this case. 
  
  #coad.maf <- GDCquery_Maf("COAD", pipelines = "muse") %>% read.maf
  coad.maf <- read.maf(GDCquery_Maf("COAD", pipelines = "muse"))
  discoversomaticInteractions(maf = coad.maf, top = 35, pvalue = c(1e-2, 2e-3))
  
## End(Not run)

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
)

Arguments

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)

Value

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

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)
  
  
  #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)
  
  
  ## Not run: 
  #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)
  
## End(Not run)
  

getMutexAB function

Description

Given two binary matrices and its corresponding probability matrices PAij and PBij, compute the Poisson Binomial method to estimate mutual exclusive events between A and B

Usage

getMutexAB(
  A,
  PMA = getPM(A),
  B,
  PMB = getPM(B),
  lower.tail = TRUE,
  method = "ShiftedBinomial",
  mixed = TRUE,
  th = 0.05,
  verbose = FALSE,
  parallel = FALSE,
  no_cores = NULL
)

Arguments

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)

Value

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

Examples

     
  
  #The next example, is the same as the first
  # one but, using a matrix of class Matrix. 
  
  data("A_Matrix")
  data("B_Matrix")
  PMA <- getPM(A_Matrix)
  PMB <- getPM(B_Matrix)
  mismutex <- getMutexAB(A=A_Matrix, PM=PMA, B=B_Matrix, PMB = PMB)
  
  #Finally, the last example, shows a 
  #real example of how to perform this function
  # when using data from TCGA, Colon Adenocarcinoma in this case. 
  
  ## Not run: 
  data("TCGA_COAD_AMP")
  data("AMP_COAD")
  data("PM_TCGA_COAD_AMP")
  data("PM_AMP_COAD")
  
  mismutex <- getMutexAB(A=TCGA_COAD_AMP, 
                         PMA=PM_TCGA_COAD_AMP,
                         B=AMP_COAD,
                         PMB = PM_AMP_COAD)
   
## End(Not run)
 

getMutexGroup function

Description

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

Usage

getMutexGroup(A = NULL, PM = NULL, type = "Impurity", lower.tail = TRUE)

Arguments

Value

A symmetric matrix with the p.value of the corresponding test.

Examples

  #This first example is a basic 
  #example of how to perform getMutexGroup
  
  data("A_example")
  A2 <- A_example[,1:30]
  A2[1,1:10] <- 1
  A2[2,1:10] <- 0
  A2[3,1:10] <- 0
  A2[1,11:20] <- 0
  A2[2,11:20] <- 1
  A2[3,11:20] <- 0
  A2[1,21:30] <- 0
  A2[2,21:30] <- 0
  A2[3,21:30] <- 1
  PM2 <- getPM(A2)
  A <- A2[1:3,]
  PM <- PM2[1:3,]
  
  getMutexGroup(A, PM, "Impurity")
  getMutexGroup(A, PM, "Coverage")
  getMutexGroup(A, PM, "Exclusivity")
  

getPM function

Description

Given a binary matrix estimates the corresponding probability matrix pij.

Usage

getPM(A)

Arguments

Value

A 'PMatrix' object with the corresponding probability estimations. This 'PMatrix' object stored the corresponding coefficients of the logistic regression computed. With this coefficients it is possible to build the complete matrix of probabilities.

Examples

  #This first example is a basic example of how to perform getPM: 
  
  data("A_example")
  PMA <- getPM(A_example)
  
  #The next example, is the same as the first one but, 
  #using a matrix of class Matrix: 
  
  data("A_Matrix")
  PMA_Matrix <- getPM(A_Matrix)
  
  ## Not run: 
  #Finally, the last example, shows a real example 
  #of how to perform this function when when using
  #data from TCGA, Colon Adenocarcinoma in this case: 
  data("TCGA_COAD")
  PM_COAD <- getPM(TCGA_COAD)
  
## End(Not run)