Package 'cancerGI' reference manual

Title:	Analyses of Cancer Gene Interaction
Description:	Functions to perform the following analyses: i) inferring epistasis from RNAi double knockdown data; ii) identifying gene pairs of multiple mutation patterns; iii) assessing association between gene pairs and survival; and iv) calculating the smallworldness of a graph (e.g., a gene interaction network). Data and analyses are described in Wang, X., Fu, A. Q., McNerney, M. and White, K. P. (2014). Widespread genetic epistasis among breast cancer genes. Nature Communications. 5 4828. <doi:10.1038/ncomms5828>.
Authors:	Audrey Qiuyan Fu and Xiaoyue Wang
Maintainer:	Audrey Q. Fu <[email protected]>
License:	GPL (>= 2)
Version:	1.0.1
Built:	2024-11-22 04:21:57 UTC
Source:	https://github.com/cran/cancerGI

Compute smallworldness of a graph

Description

This function computes the smallworldness of a graph.

Usage

computeSmallWorldness(g, n, m, nrep = 1000)
computeSmallWorldness(g, n, m, nrep = 1000)

Arguments

`g`	A graph object.
`n`	Number of nodes of g.
`m`	Number of edges of g.
`nrep`	Number of random graphs to generate for estimating $C_{rand}$ and $L_{rand}$ .

Details

For a graph $g$ with $n$ nodes and $m$ edges, the smallworldness $S$ is defined as in Humphries and Gurney (2008):

$S = (C_g / C_{rand}) / (L_g / L_{rand})$ ,

where $C_g$ and $C_{rand}$ are the clustering coefficient of $g$ and that of a random graph with the same number of nodes and edges as $g$ , respectively. Also, $L_g$ and $L_{rand}$ are the mean shortest path length of $g$ and that of the same random graph, respectively.

Here, in order to estimate $C_{rand}$ and $L_{rand}$ , this function generates a large number of random graphs with $n$ nodes and $m$ edges under the Erdos-Renyi model (Erdos and Renyi, 1959), such that each edge is created with the same probability as the nodes in $g$ . This function then computes $C$ and $L$ for each random graph, and takes the average as the estimate for $C_{rand}$ and $L_{rand}$ .

Value

A scalar of smallworldness.

Author(s)

Audrey Q. Fu

References

Humphries, M. D. and Gurney, K. Network 'small-world-ness': a quantitative method for determining canonical network equivalence. PLoS ONE 3, e0002051 (2008).

Erdos, P. and Renyi, A. On random graphs. Publ. Math. 6, 290-297 (1959).

Wang, X., Fu, A. Q., McNerney, M. and White, K. P. (2014). Widespread genetic epistasis among breast cancer genes. Nature Communications. 5 4828. doi: 10.1038/ncomms5828

Examples

library (igraph)
# compute smallworldness for the design graph
data (tested_pairs)
# build the graph object
g <- graph.edgelist (as.matrix (tested_pairs), directed=FALSE)
summary (g)  # 67 nodes and 1508 edges
# compute smallworldness
computeSmallWorldness (g, n=67, m=1508)
library (igraph)
# compute smallworldness for the design graph
data (tested_pairs)
# build the graph object
g <- graph.edgelist (as.matrix (tested_pairs), directed=FALSE)
summary (g)  # 67 nodes and 1508 edges
# compute smallworldness
computeSmallWorldness (g, n=67, m=1508)

Survival analysis for pairs of genes

Description

This function counts the number of individuals with different mutation patterns, estimates the median survival time for each mutation pattern, and computes the p values.

Usage

computeSurvivalPValueForGenePairSet.output(file.out, 
	gene.pairs, data.mut, data.surv, 
	colTime = 2, colStatus = 3, 
	type.gene1 = (-1), type.gene2 = (-1), 
	groups = c("All", "Two"), 
	PRINT = FALSE, PRINT.INDEX = FALSE)
computeSurvivalPValueForGenePairSet.output(file.out, 
	gene.pairs, data.mut, data.surv, 
	colTime = 2, colStatus = 3, 
	type.gene1 = (-1), type.gene2 = (-1), 
	groups = c("All", "Two"), 
	PRINT = FALSE, PRINT.INDEX = FALSE)

Arguments

`file.out`	Output filename.
`gene.pairs`	Matrix of two columns, which are gene names.
`data.mut`	Integer matrix of genes by cases. The first column contains gene names. Each of the other columns contains mutation patterns of a case: 0 as wildtype, 1 amplification and -1 deletion.
`data.surv`	Data frame containing case ID, survival time and survival status. Cases do not need to match those in `data.mut`.
`colTime`	Scalar indicating which column in `data.surv` contains the survival time.
`colStatus`	A character string indicating which column in `data.surv` contains the survival status: "DECEASED" or "LIVING".
`type.gene1`	Integer indicating the type of mutation: 0 for wild type, 1 for amplification, and -1 for deletion.
`type.gene2`	Same as `type.gene1`, but for the second gene.
`groups`	"All" if comparing all combinations: wildtype & wildtype, wild type & mutated, both mutated; or "Two", if only comparing single mutation and double mutation.
`PRINT`	Default is FALSE. Prints intermediate values if set to TRUE. Output may be massive if the number of gene pairs is large.
`PRINT.INDEX`	Default is FALSE. Unused.

Value

Data frame containing the following columns (if groups="Two"):

`gene1`
`gene2`
`nSingleMut`	No. of cases with single mutation
`nDoubleMut`	No. of cases with double mutation
`obsSingleMut`	No. of deceased cases with single mutation
`obsDoubleMut`	No. of deceased cases with double mutation
`expSingleMut`	Expected no. of deceased cases with single mutation
`expDbouleMut`	Expected no. of deceased cases with double mutation
`medianSingleMut`	Estimated median survival time for single mutation
`medianDoubleMut`	Estimated median survival time for double mutation
`pValue`	p value for testing whether double/single mutation is associated with survival

Author(s)

Audrey Q. Fu, Xiaoyue Wang

References

Wang, X., Fu, A. Q., McNerney, M. and White, K. P. (2014). Widespread genetic epistasis among breast cancer genes. Nature Communications. 5 4828. doi: 10.1038/ncomms5828

Examples

## Not run: 
data (mutations)
data (survival)

# compute p values for gene pairs tested in the RNAi knockdown assay
data (tested_pairs)

# compute p values for the gain & loss combination
# and compare only cases of single mutations with cases of double mutations;
# results are written to file tmp.txt under current directory
computeSurvivalPValueForGenePairSet.output (file.out="tmp.txt", 
	tested_pairs, data.mut=mutations, data.surv=survival, 
	type.gene1=1, type.gene2=(-1), groups="Two")


## End(Not run)
## Not run: 
data (mutations)
data (survival)

# compute p values for gene pairs tested in the RNAi knockdown assay
data (tested_pairs)

# compute p values for the gain & loss combination
# and compare only cases of single mutations with cases of double mutations;
# results are written to file tmp.txt under current directory
computeSurvivalPValueForGenePairSet.output (file.out="tmp.txt", 
	tested_pairs, data.mut=mutations, data.surv=survival, 
	type.gene1=1, type.gene2=(-1), groups="Two")


## End(Not run)

Survival analysis for pairs of genes (with matched individuals)

Description

This function is similar to computeSurvivalPValueForGenePairSet.output, except that individuals in data.mut and data.surv should match, and that gene.pairs contains four columns: gene1, mutation type of gene1, gene2, mutation type of gene2.

Usage

computeSurvivalPValueGenePairAll.output(file.out, 
	gene.pairs, data.mut, data.surv, 
	colTime = 2, colStatus = 3, 
	groups = c("All", "Two"), 
	PRINT = FALSE, PRINT.INDEX = FALSE)
computeSurvivalPValueGenePairAll.output(file.out, 
	gene.pairs, data.mut, data.surv, 
	colTime = 2, colStatus = 3, 
	groups = c("All", "Two"), 
	PRINT = FALSE, PRINT.INDEX = FALSE)

Arguments

`file.out`	Output filename.
`gene.pairs`	Matrix of four columns: gene1, mutation type of gene1, gene2, mutation type of gene2.
`data.mut`	Integer matrix of genes by cases. The first column contains gene names. Each of the other columns contains mutation patterns of a case: 0 as wildtype, 1 amplification and -1 deletion.
`data.surv`	Data frame containing case ID, survival time and survival status. Cases should match those in `data.mut`.
`colTime`	Scalar indicating which column in `data.surv` contains the survival time.
`colStatus`	A character string indicating which column in `data.surv` contains the survival status: "DECEASED" or "LIVING".
`groups`	"All" if comparing all combinations: wildtype & wildtype, wild type & mutated, both mutated; or "Two", if only comparing single mutation and double mutation.
`PRINT`	Default is FALSE. Prints intermediate values if set to TRUE. Output may be massive if the number of gene pairs is large.
`PRINT.INDEX`	Default is FALSE. Unused.

Author(s)

Audrey Q. Fu, Xiaoyue Wang

References

Wang, X., Fu, A. Q., McNerney, M. and White, K. P. (2014). Widespread genetic epistasis among breast cancer genes. Nature Communications. 5 4828. doi: 10.1038/ncomms5828

Survival analysis for one pair of genes

Description

This function performs survival analysis, similar to function computeSurvivalPValueForGenePairSet.output, but for one pair of genes.

Usage

computeSurvivalPValueOneGenePair(data.mut, data.surv, 
	colTime = 2, colStatus = 3, 
	type.gene1 = (-1), type.gene2 = (-1), 
	groups = c("All", "Two"), 
	compare = c("Both", "Gene1", "Gene2"), 
	PLOT = FALSE, PRINT = FALSE, 
	pvalue.text.x = 10, pvalue.text.y = 0.1, 
	legend.x = 150, legend.y = 1)
computeSurvivalPValueOneGenePair(data.mut, data.surv, 
	colTime = 2, colStatus = 3, 
	type.gene1 = (-1), type.gene2 = (-1), 
	groups = c("All", "Two"), 
	compare = c("Both", "Gene1", "Gene2"), 
	PLOT = FALSE, PRINT = FALSE, 
	pvalue.text.x = 10, pvalue.text.y = 0.1, 
	legend.x = 150, legend.y = 1)

Arguments

`data.mut`	Integer matrix of individuals by two genes. Each column containing the mutation patterns of multiple genes: 0 as wildtype, 1 amplification and -1 deletion.
`data.surv`	Data frame containing case ID, survival time and survival status. Cases should match those in `data.mut`.
`colTime`	Scalar indicating which column in `data.surv` contains the survival time.
`colStatus`	A character string indicating which column in `data.surv` contains the survival status: "DECEASED" or "LIVING".
`type.gene1`	Integer indicating the type of mutation: 0 for wild type, 1 for amplification, and -1 for deletion.
`type.gene2`	Same as `type.gene1`, but for the second gene.
`groups`	"All" if comparing all combinations: wildtype & wildtype, wild type & mutated, both mutated; or "Two", if only comparing single mutation and double mutation.
`compare`	"Both" if comparing all four combinations: wildtype & wildtype, wildtype & mutated, mutated & wildtype, and mutated & mutated. "Gene1" if comparing three combinations: gene1 wildtype, gene1 mutated & gene2 wildtype, and both mutated. "Gene2" is similar to "Gene1".
`PLOT`	If TRUE, plot the survival curves and print the p value onto the plot. Location of the p value legend is controlled by `pvalue.text.x` and `pvalue.text.y` described below.
`PRINT`	If TRUE, print intermediate values.
`pvalue.text.x`	The x coordinate of the p value legend in plot.
`pvalue.text.y`	The y coordinate of the p value legend in plot.
`legend.x`	The x coordinate of the curve legend in plot.
`legend.y`	The y coordinate of the curve legend in plot.

Value

The output contains the same info as described in computeSurvivalPValueForGenePairSet.output.

Author(s)

Audrey Q. Fu, Xiaoyue Wang

References

Wang, X., Fu, A. Q., McNerney, M. and White, K. P. (2014). Widespread genetic epistasis among breast cancer genes. Nature Communications. 5 4828. doi: 10.1038/ncomms5828

Write results from survival analysis to output for one pair of genes

Description

This function is similar to computeSurvivalPValueOneGenePair, except that it writes the analysis results directly to output file and does not allow for plotting the survival curves.

Usage

computeSurvivalPValueOneGenePair.output(file.out, genes.info, 
	data.mut, data.surv, colTime = 2, colStatus = 3, 
	groups = c("All", "Two"), PRINT = FALSE)
computeSurvivalPValueOneGenePair.output(file.out, genes.info, 
	data.mut, data.surv, colTime = 2, colStatus = 3, 
	groups = c("All", "Two"), PRINT = FALSE)

Arguments

`file.out`	Output filename.
`genes.info`	A vector of 6 elements: gene1, mutation type, gene2, mutation type, gene1's column index in `data.mut`, gene2's column index in `data.mut`.
`data.mut`	Integer matrix of genes by cases. The first column contains gene names. Each of the other columns contains mutation patterns of a case: 0 as wildtype, 1 amplification and -1 deletion.
`data.surv`	Data frame containing case ID, survival time and survival status. Cases should match those in `data.mut`.
`colTime`	Scalar indicating which column in `data.surv` contains the survival time.
`colStatus`	A character string indicating which column in `data.surv` contains the survival status: "DECEASED" or "LIVING".
`groups`	"All" if comparing all combinations: wildtype & wildtype, wild type & mutated, both mutated; or "Two", if only comparing single mutation and double mutation.
`PRINT`	Default is FALSE. Prints intermediate values if set to TRUE. Output may be massive if the number of gene pairs is large.

Value

A vector of values from the survival analysis, as described in computeSurvivalPValueForGenePairSet.output

Author(s)

Audrey Q. Fu, Xiaoyue Wang

References

Wang, X., Fu, A. Q., McNerney, M. and White, K. P. (2014). Widespread genetic epistasis among breast cancer genes. Nature Communications. 5 4828. doi: 10.1038/ncomms5828

Generate a design matrix from raw RNAi data.

Description

This function takes the raw RNAi data as input and generates a design matrix for regression. Specifically written for the format of the data set RNAi, which contains four batches. This R function will use batch3 as the baseline.

Usage

constructDesignMatrix(data, covariates)
constructDesignMatrix(data, covariates)

Arguments

`data`	Matrix of RNAi measurements; includes columns batch, query_gene and template_gene.
`covariates`	Vector of strings; each string is the name of a covariate.

Value

A design matrix. The number of rows is the same as that of the data set RNAi, and the number of columns is the same as the length of covariates.

Examples

## See example in documentation for the data set RNAi.
## See example in documentation for the data set RNAi.

Genetic mutation data in patients.

Description

Data frame that contains mutation patterns in multiple genes across multiple patients.

Format

A data frame with 85 rows and 951 columns. Each row is a gene. The first column contains gene names, and each of the other columns contains the mutation pattern in an individual: 0 for no mutation, 1 amplification and -1 deletion.

References

Wang, X., Fu, A. Q., McNerney, M. and White, K. P. (2014). Widespread genetic epistasis among breast cancer genes. Nature Communications. 5 4828. doi: 10.1038/ncomms5828.

Examples

data(mutations)
data(mutations)

Find matched individuals in mutation and survival data

Description

This functions finds matched individuals in data.mut and data.surv, and outputs the two data sets with only matched individuals.

Usage

processDataMutSurv(data.mut, data.surv, colTime = 2, colStatus = 3)
processDataMutSurv(data.mut, data.surv, colTime = 2, colStatus = 3)

Arguments

`data.mut`	Integer matrix of genes by cases. The first column contains gene names. Each of the other columns contains mutation patterns of a case: 0 as wildtype, 1 amplification and -1 deletion.
`data.surv`	Data frame containing case ID, survival time and survival status. Cases do not need to match those in `data.mut`.
`colTime`	Scalar indicating which column in `data.surv` contains the survival time.
`colStatus`	A character string indicating which column in `data.surv` contains the survival status: "DECEASED" or "LIVING".

Value

A list of two data frames, data.mut and data.surv. Format of the data frames is the same as input, except that the individuals in the two data frames are matched.

Author(s)

Audrey Q. Fu, Xiaoyue Wang

References

Wang, X., Fu, A. Q., McNerney, M. and White, K. P. (2014). Widespread genetic epistasis among breast cancer genes. Nature Communications. 5 4828. doi: 10.1038/ncomms5828

Molecular phenotypes from single and double knockdowns in RNAi screen

Description

Single and double siRNA knockdowns were performed for genes and gene pairs. Multiple molecular phenotypes, such as the number of cells, cell size, nucleus size, etc., were measured.

Value

A data matrix with each row a knockdown experiment.

References

Wang, X., Fu, A. Q., McNerney, M. and White, K. P. (2014). Widespread genetic epistasis among breast cancer genes. Nature Communications. 5 4828. doi: 10.1038/ncomms5828

Examples

## Not run: 
library (systemfit)
library (qvalue)

data (RNAi)
data (tested_pairs) # gene pairs tested in the RNAi knockdown assay

# extract gene names and put in a vector
genelist <- union(unique(RNAi$template_gene),unique(RNAi$query_gene))
genelist <- genelist[!((genelist=="empty")|(genelist=="NT"))]

# create the interaction terms for linear model
sorted_tested_pairs <- apply(tested_pairs,1,
	function(x){if (x[1]>x[2]) return (c(x[2],x[1])) 
	else return(c(x[1],x[2]))})
pairs_names <- apply(sorted_tested_pairs,2,
	function(x) {paste(x[1],x[2],sep=":")})

# create vector of covariates
# using batch3 as baseline
regressors <- c("batch1","batch2","batch4",genelist,pairs_names)

# construct the design matrix
my_matrix=constructDesignMatrix(data=RNAi, covariates=regressors)

# n (cell number) and csize (cell size) are on log2 scale already
# need to transform nsize (nucleus size) to original scale
RNAi.tmp <- RNAi
RNAi$nsize <- 2^RNAi.tmp$nsize
rm (RNAi.tmp)

# create formula from column names
# using all columns
#eqlog2n <- as.formula (paste ("RNAi$n ~ ", 
#	paste (colnames (my_matrix), collapse="+"), sep=''))
#eqlog2csize <- as.formula (paste ("RNAi$csize ~ ", 
#	paste (colnames (my_matrix), collapse="+"), sep=''))
#eqnsize <- as.formula (paste ("RNAi$nsize ~ ", 
#	paste (colnames (my_matrix), collapse="+"), sep=''))
	
# test run with the first 500 columns
eqlog2n <- as.formula (paste ("RNAi$n ~ ", 
	paste (colnames (my_matrix)[1:500], collapse="+"), sep=''))
eqlog2csize <- as.formula (paste ("RNAi$csize ~ ", 
	paste (colnames (my_matrix)[1:500], collapse="+"), sep=''))
eqnsize <- as.formula (paste ("RNAi$nsize ~ ", 
	paste (colnames (my_matrix)[1:500], collapse="+"), sep=''))
	
system <- list (cell.number = eqlog2n, cell.size = eqlog2csize, nuc.size=eqnsize)

# perform seemingly unrelated regression
fitsur <- systemfit (system, "SUR", data=cbind (RNAi, my_matrix), maxit=100)

# extract coefficient estimates
log2n_fitsur_coef <- coef (summary (fitsur$eq[[1]]))
log2csize_fitsur_coef <- coef (summary (fitsur$eq[[2]]))
nsize_fitsur_coef <- coef (summary (fitsur$eq[[3]]))

# compute q values
log2n_coef_q <- qvalue (log2n_fitsur_coef[,4])$qvalues
log2csize_coef_q <- qvalue (log2csize_fitsur_coef[,4])$qvalues
nsize_coef_q <- qvalue (nsize_fitsur_coef[,4])$qvalues

# build three matrices of results
log2n_fitsur_coef <- data.frame (log2n_fitsur_coef, qvalue=log2n_coef_q)
colnames (log2n_fitsur_coef) <- c("Estimate", "StdError", "tValue", "pValue", "qValue")
dim (log2n_fitsur_coef)
head (log2n_fitsur_coef)

log2csize_fitsur_coef <- data.frame (log2csize_fitsur_coef, qvalue=log2csize_coef_q)
colnames (log2csize_fitsur_coef) <- c("Estimate", "StdError", "tValue", "pValue", "qValue")
dim (log2csize_fitsur_coef)
head (log2csize_fitsur_coef)

nsize_fitsur_coef <- data.frame (nsize_fitsur_coef, qvalue=nsize_coef_q)
colnames (nsize_fitsur_coef) <- c("Estimate", "StdError", "tValue", "pValue", "qValue")
dim (nsize_fitsur_coef)
head (nsize_fitsur_coef)

## End(Not run)
## Not run: 
library (systemfit)
library (qvalue)

data (RNAi)
data (tested_pairs) # gene pairs tested in the RNAi knockdown assay

# extract gene names and put in a vector
genelist <- union(unique(RNAi$template_gene),unique(RNAi$query_gene))
genelist <- genelist[!((genelist=="empty")|(genelist=="NT"))]

# create the interaction terms for linear model
sorted_tested_pairs <- apply(tested_pairs,1,
	function(x){if (x[1]>x[2]) return (c(x[2],x[1])) 
	else return(c(x[1],x[2]))})
pairs_names <- apply(sorted_tested_pairs,2,
	function(x) {paste(x[1],x[2],sep=":")})

# create vector of covariates
# using batch3 as baseline
regressors <- c("batch1","batch2","batch4",genelist,pairs_names)

# construct the design matrix
my_matrix=constructDesignMatrix(data=RNAi, covariates=regressors)

# n (cell number) and csize (cell size) are on log2 scale already
# need to transform nsize (nucleus size) to original scale
RNAi.tmp <- RNAi
RNAi$nsize <- 2^RNAi.tmp$nsize
rm (RNAi.tmp)

# create formula from column names
# using all columns
#eqlog2n <- as.formula (paste ("RNAi$n ~ ", 
#	paste (colnames (my_matrix), collapse="+"), sep=''))
#eqlog2csize <- as.formula (paste ("RNAi$csize ~ ", 
#	paste (colnames (my_matrix), collapse="+"), sep=''))
#eqnsize <- as.formula (paste ("RNAi$nsize ~ ", 
#	paste (colnames (my_matrix), collapse="+"), sep=''))
	
# test run with the first 500 columns
eqlog2n <- as.formula (paste ("RNAi$n ~ ", 
	paste (colnames (my_matrix)[1:500], collapse="+"), sep=''))
eqlog2csize <- as.formula (paste ("RNAi$csize ~ ", 
	paste (colnames (my_matrix)[1:500], collapse="+"), sep=''))
eqnsize <- as.formula (paste ("RNAi$nsize ~ ", 
	paste (colnames (my_matrix)[1:500], collapse="+"), sep=''))
	
system <- list (cell.number = eqlog2n, cell.size = eqlog2csize, nuc.size=eqnsize)

# perform seemingly unrelated regression
fitsur <- systemfit (system, "SUR", data=cbind (RNAi, my_matrix), maxit=100)

# extract coefficient estimates
log2n_fitsur_coef <- coef (summary (fitsur$eq[[1]]))
log2csize_fitsur_coef <- coef (summary (fitsur$eq[[2]]))
nsize_fitsur_coef <- coef (summary (fitsur$eq[[3]]))

# compute q values
log2n_coef_q <- qvalue (log2n_fitsur_coef[,4])$qvalues
log2csize_coef_q <- qvalue (log2csize_fitsur_coef[,4])$qvalues
nsize_coef_q <- qvalue (nsize_fitsur_coef[,4])$qvalues

# build three matrices of results
log2n_fitsur_coef <- data.frame (log2n_fitsur_coef, qvalue=log2n_coef_q)
colnames (log2n_fitsur_coef) <- c("Estimate", "StdError", "tValue", "pValue", "qValue")
dim (log2n_fitsur_coef)
head (log2n_fitsur_coef)

log2csize_fitsur_coef <- data.frame (log2csize_fitsur_coef, qvalue=log2csize_coef_q)
colnames (log2csize_fitsur_coef) <- c("Estimate", "StdError", "tValue", "pValue", "qValue")
dim (log2csize_fitsur_coef)
head (log2csize_fitsur_coef)

nsize_fitsur_coef <- data.frame (nsize_fitsur_coef, qvalue=nsize_coef_q)
colnames (nsize_fitsur_coef) <- c("Estimate", "StdError", "tValue", "pValue", "qValue")
dim (nsize_fitsur_coef)
head (nsize_fitsur_coef)

## End(Not run)

Patient survival data.

Description

Data set that contains the survival time (in months), survival status and other information of patients.

Format

A data frame with 950 observations on the following 5 variables.

CaseID: A vector of character strings
OverallSurvivalMonths: A numeric vector
OverallSurvivalStatus: A factor with levels DECEASED LIVING
MutationCount: A numeric vector
FractionOfCopyNumberAlteredGenome: A numeric vector

Source

Data were downloaded from http://www.cbioportal.org/.

References

Data were described and analyzed in Wang, X., Fu, A. Q., McNerney, M. and White, K. P. (2014). Widespread genetic epistasis among breast cancer genes. Nature Communications. 5 4828. doi: 10.1038/ncomms5828.

Examples

data(survival)
data(survival)

Gene pairs tested in the double knockdown assay.

Description

It contains two columns of gene names.

Format

A data frame with 1508 observations on the following 2 variables.

V1: a character vector
V2: a character vector

References

Wang, X., Fu, A. Q., McNerney, M. and White, K. P. (2014). Widespread genetic epistasis among breast cancer genes. Nature Communications. 5 4828. doi: 10.1038/ncomms5828

Examples

data(tested_pairs)
## see documentation for dataset \code{\link{RNAi}}
data(tested_pairs)
## see documentation for dataset \code{\link{RNAi}}

Compute the p and q values of all pairwise gene mutation patterns

Description

This function computes the p and q values of all pairwise gene mutation patterns. Patterns include both genes losing their function, one gene gaining function and the other losing function, both genes gaining function, and the two genes being mutually exclusive.

Usage

testMutationalPatternAll.wrapper(data, QVALUE = TRUE, PRINT = FALSE)
testMutationalPatternAll.wrapper(data, QVALUE = TRUE, PRINT = FALSE)

Arguments

`data`	Matrix of gene mutations. Each row is a gene. The first column contains gene names, and all the other columns each contain mutation values in an individual. Value 1 corresponds to gain of function, -1 loss of function, and 0 no change. Missing values are denoted NAs.
`QVALUE`	TRUE if q values are calculated, and FALSE otherwise.
`PRINT`	TRUE if printing intermediate values, and FALSE otherwise.

Value

A list of two matrices, one containing the p values, and the other the q values (if the QVALUE argument set to TRUE). Each matrix has the following columns: gene 1, gene 2, p (or q) value of the loss & loss, gain & loss, loss & gain, gain & gain, and mutually exclusive combination.

Author(s)

Audrey Fu, Xiaoyue Wang

References

Wang, X., Fu, A. Q., McNerney, M. and White, K. P. (2014). Widespread genetic epistasis among breast cancer genes. Nature Communications. 5 4828. doi: 10.1038/ncomms5828

Examples

data (mutations)
mut.pqvalues <- testMutationalPatternAll.wrapper (data=mutations, QVALUE=TRUE)
summary (mut.pqvalues)
dim (mut.pqvalues$pvalues)
dim (mut.pqvalues$qvalues)
mut.pqvalues$pvalues[1:10,]
data (mutations)
mut.pqvalues <- testMutationalPatternAll.wrapper (data=mutations, QVALUE=TRUE)
summary (mut.pqvalues)
dim (mut.pqvalues$pvalues)
dim (mut.pqvalues$qvalues)
mut.pqvalues$pvalues[1:10,]

Package 'cancerGI'

Help Index

Compute smallworldness of a graph

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Survival analysis for pairs of genes

Description

Usage

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Value

Author(s)

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Examples

Survival analysis for pairs of genes (with matched individuals)

Description

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Author(s)

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Survival analysis for one pair of genes

Description

Usage

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Value

Author(s)

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Write results from survival analysis to output for one pair of genes

Description

Usage

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Author(s)

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Generate a design matrix from raw RNAi data.

Description

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Value

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Genetic mutation data in patients.

Description

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Find matched individuals in mutation and survival data

Description

Usage

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Value

Author(s)

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Molecular phenotypes from single and double knockdowns in RNAi screen

Description

Value

References

Examples

Patient survival data.

Description

Format

Source

References

Examples

Gene pairs tested in the double knockdown assay.

Description

Format

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Examples

Compute the p and q values of all pairwise gene mutation patterns

Description

Usage