Package 'epoc' reference manual

Title:	Endogenous Perturbation Analysis of Cancer
Description:	Estimates sparse matrices A or G using fast lasso regression from mRNA transcript levels Y and CNA profiles U. Two models are provided, EPoC A where AY + U + R = 0 and EPoC G where Y = GU + E, the matrices R and E are so far treated as noise. For details see the manual page of 'lassoshooting' and the article Rebecka Jörnsten, Tobias Abenius, Teresia Kling, Linnéa Schmidt, Erik Johansson, Torbjörn E M Nordling, Bodil Nordlander, Chris Sander, Peter Gennemark, Keiko Funa, Björn Nilsson, Linda Lindahl, Sven Nelander (2011) <doi:10.1038/msb.2011.17>.
Authors:	Rebecka Jornsten, Tobias Abenius, Sven Nelander
Maintainer:	Tobias Abenius <[email protected]>
License:	LGPL-3
Version:	0.2.6-1.1
Built:	2024-11-13 06:46:33 UTC
Source:	CRAN

EPoC

Description

EPoC (Endogenous Perturbation analysis of Cancer)

Usage

epocA(Y, U=NULL, lambdas=NULL, thr=1.0e-10, trace=0, ...)
epocG(Y, U, lambdas=NULL, thr=1.0e-10, trace=0, ...)
epoc.lambdamax(X, Y, getall=F, predictorix=NULL)
as.graph.EPOCA(model,k=1)
as.graph.EPOCG(model,k=1)
write.sif(model, k=1, file="", append=F)
## S3 method for class 'EPOCA'
print(x,...)
## S3 method for class 'EPOCG'
print(x,...)
## S3 method for class 'EPOCA'
summary(object, k=NULL, ...)
## S3 method for class 'EPOCG'
summary(object, k=NULL,...)
## S3 method for class 'EPOCA'
coef(object, k=1, ...)
## S3 method for class 'EPOCG'
coef(object, k=1, ...)
## S3 method for class 'EPOCA'
predict(object, newdata,k=1,trace=0, ...)
## S3 method for class 'EPOCG'
predict(object, newdata,k=1,trace=0, ...)
epocA(Y, U=NULL, lambdas=NULL, thr=1.0e-10, trace=0, ...)
epocG(Y, U, lambdas=NULL, thr=1.0e-10, trace=0, ...)
epoc.lambdamax(X, Y, getall=F, predictorix=NULL)
as.graph.EPOCA(model,k=1)
as.graph.EPOCG(model,k=1)
write.sif(model, k=1, file="", append=F)
## S3 method for class 'EPOCA'
print(x,...)
## S3 method for class 'EPOCG'
print(x,...)
## S3 method for class 'EPOCA'
summary(object, k=NULL, ...)
## S3 method for class 'EPOCG'
summary(object, k=NULL,...)
## S3 method for class 'EPOCA'
coef(object, k=1, ...)
## S3 method for class 'EPOCG'
coef(object, k=1, ...)
## S3 method for class 'EPOCA'
predict(object, newdata,k=1,trace=0, ...)
## S3 method for class 'EPOCG'
predict(object, newdata,k=1,trace=0, ...)

Arguments

`Y`	N x p matrix of mRNA transcript levels for p genes and N samples for epocA and epocG. For `epoc.lambdamax` `Y` is a multi-response matrix
`U`	N x p matrix of DNA copy number
`lambdas`	Non-negative vector of relative regularization parameters for lasso. $\lambda=0$ means no regularization which give dense solutions (and takes longer to compute). Default=NULL means let EPoC create a vector
`thr`	Threshold for convergence. Default value is 1e-10. Iterations stop when max absolute parameter change is less than thr
`trace`	Level of detail for printing out information as iterations proceed. Default 0 – no information
`X`	In `epoc.lambdamax` `X` is the design matrix, i.e. predictors
`predictorix`	For `epoc.lambdamax` when using a multi-response matrix `Y` predictors are set to zero for each corresponding response. `predictorix` tells which of the responses that have a corresponding predictor in the network case
`getall`	Logical. For `epoc.lambdamax` get a vector of all inf-norms instead of a single maximum
`file`	either a character string naming a file or a connection open for writing. `""` indicates output to the console
`append`	logical. Only relevant if `file` is a character string. If `TRUE`, the output is appended to the file. If `FALSE`, any existing file of the name is destroyed
`model`	Model set from epocA or epocG
`k`	Select a model of sparsity level k in [1,K]. In `summary` default (NULL) means all. In `plot` default is first model.
`newdata`	List of Y and U matrices required for prediction. `epocG` requires just U.
`x`	Model parameter to `print` and `plot`
`object`	Model parameter to `summary`, `coef` and `predict`
`...`	Parameters passed down to underlying function, e.g. `print.default`. For `epocA` and `epocG` `...` are reserved for experimental options.

Details

epocA and epocG estimates sparse matrices $A$ or $G$ using fast lasso regression from mRNA transcript levels $Y$ and CNA profiles $U$ . Two models are provided, EPoC A where

$AY + U + R = 0$

and EPoC G where

$Y = GU + E.$

The matrices $R$ and $E$ are so far treated as noise. For details see the reference section and the manual page of lassoshooting.

If you have different sizes of U and Y you need to sort your Y such that the U-columns correspond to the first Y-columns. Example: Y.new <- cbind(Y[,haveCNA], Y[, -haveCNA]) CHANGES: predictorix used to be a parameter with a vector of a subset of the variables 1:p of U corresponding to transcripts in Y, Default was to use all which mean that Y and U must have same size.

epoc.lambdamax returns the maximal $\lambda$ value in a series of lasso regression models such that all coefficients are zero.

plot if type='graph' (default) plot graph of model using the Rgraphviz package arrows only tell direction, not inhibit or stimulate. If type='modelsel' see modelselPlot.

Value

epocA and epocG returns an object of class ‘"epocA"’ and ‘"epocG"’ respectively.

The methods summary, print, coef, predict can be used as with other models. coef and predict take an extra optional integer parameter k (default 1) which gives the model at the given density level.

An object of these classes is a list containing at least the following components:

`coefficients`	list of t(A) or t(G) matrices for the different $\lambda$ s
`links`	the number of links for the different $\lambda$ s
`lambdas`	the $\lambda$ s used for this model
`R2`	R², coefficient of determination
`Cp`	Mallows Cp
`s2`	Estimate of the error variance
`RSS`	Residual Sum of Squares (SSreg)
`SS.tot`	Total sum of squares of the response
`inorms`	the infinity norm of predictors transposed times response for the different responses
`d`	Direct effects of CNA to corresponding gene

Note

The coef function returns transposed versions of the matrices $A$ and $G$ .

Author(s)

Tobias Abenius

References

Rebecka Jörnsten, Tobias Abenius, Teresia Kling, Linnéa Schmidt, Erik Johansson, Torbjörn Nordling, Bodil Nordlander, Chris Sander, Peter Gennemark, Keiko Funa, Björn Nilsson, Linda Lindahl, Sven Nelander. (2011) Network modeling of the transcriptional effects of copy number aberrations in glioblastoma. Molecular Systems Biology 7 (to appear)

Examples

## Not run: 
modelA <- epocA(X,U)
modelG <- epocG(X,U)

# plot sparsest A and G models using the igraph package
# arrows only tell direction, not inhibit or stimulate
par(mfrow=c(1,2))
plot(modelA)
plot(modelG)

# OpenGL 3D plot on sphere using the igraph and rgl packages
plot(modelA,threed=T)

# Write the graph to a file in SIF format for import in e.g. Cytoscape
write.sif(modelA,file="modelA.sif")

# plot graph in Cytoscape using Cytoscape XMLRPC plugin and 
# R packages RCytoscape, bioconductor graph, XMLRPC
require('graph')
require('RCytoscape')
g <- as.graph.EPOCA(modelA,k=5)
cw <- CytoscapeWindow("EPoC", graph = g)
displayGraph(cw)

# prediction
N <- dim(X)[1]
ii <- sample(1:N, N/3)

modelG <- epocG(X[ii,], U[ii,])
K <- length(modelA$lambda) # densest model index index
newdata <- list(U=U[-ii,])
e <- X[-ii,] - predict(modelA, newdata, k=K)
RSS <- sum(e^2)
cat("RMSD:", sqrt(RSS/N), "\n")


## End(Not run)
## Not run: 
modelA <- epocA(X,U)
modelG <- epocG(X,U)

# plot sparsest A and G models using the igraph package
# arrows only tell direction, not inhibit or stimulate
par(mfrow=c(1,2))
plot(modelA)
plot(modelG)

# OpenGL 3D plot on sphere using the igraph and rgl packages
plot(modelA,threed=T)

# Write the graph to a file in SIF format for import in e.g. Cytoscape
write.sif(modelA,file="modelA.sif")

# plot graph in Cytoscape using Cytoscape XMLRPC plugin and 
# R packages RCytoscape, bioconductor graph, XMLRPC
require('graph')
require('RCytoscape')
g <- as.graph.EPOCA(modelA,k=5)
cw <- CytoscapeWindow("EPoC", graph = g)
displayGraph(cw)

# prediction
N <- dim(X)[1]
ii <- sample(1:N, N/3)

modelG <- epocG(X[ii,], U[ii,])
K <- length(modelA$lambda) # densest model index index
newdata <- list(U=U[-ii,])
e <- X[-ii,] - predict(modelA, newdata, k=K)
RSS <- sum(e^2)
cat("RMSD:", sqrt(RSS/N), "\n")


## End(Not run)

epoc.bootstrap

Description

Bootstrap for the EPoC methods

Usage

  epoc.bootstrap(Y, U, nboots=100, bthr=NULL, method='epocG',...)
  ## S3 method for class 'bootsize'
plot(x, lambda.boot=NULL, B, range=c(0,1), ...)
  epoc.final(epocboot, bthr=0.2, k)
epoc.bootstrap(Y, U, nboots=100, bthr=NULL, method='epocG',...)
  ## S3 method for class 'bootsize'
plot(x, lambda.boot=NULL, B, range=c(0,1), ...)
  epoc.final(epocboot, bthr=0.2, k)

Arguments

`Y`	mRNA, samples x genes.
`U`	CNA, samples x genes.
`nboots`	Number of bootstrap iterations to run.
`method`	For `epoc.bootstrap` method is `"epocG"` or `"epocA"`.
`x`	A sparse network matrix or a list of the same, non-zeros are links. These come from e.g. `epoc.final` or `epoc.bootstrap`.
`lambda.boot`	The $\lambda$ s used to run the bootstrap.
`B`	Number of bootstrap iterations ran for `'plot.bootsize'`.
`range`	Range of bootstrap thresholds to display.
`epocboot`	For `epoc.final` give a list of bootstraped models from `epoc.bootstrap`.
`k`	For `epoc.final` and `epoc.bootplot` select the `k` sparsest model.
`bthr`	Require presence of links in 100*bthr% of the bootstrap iterations.
`...`	Parameters passed down to an underlying function. For `epoc.bootstrap` these are passed down to `"epocG"` or `"epocA"` respectively. For `epoc.bootplot` and `plot.bootsize` parameters are passed down to the underlying plot command.

Details

epoc.bootstrap run epocA or epocG using bootstrap.

Value

epoc.bootstrap returns a list of $p \times p$ arrays of values in $[0,1]$ where 1 is presence of link in 100% of bootstrap iterations for the $k$ different $\lambda$ values for $p$ different genes. epoc.final returns a sparse matrix of $p \times p$ values in $[0,1]$ where 1 is presence of link in 100% of bootstrap iterations, but thresholded such that all values have be greater than or equal to bthr.

References

epoc.survival

Description

Survival analysis

Usage

  epoc.svd(model, k=1, C=1, numload=NULL)
  epoc.survival(G.svd, Y, U, surv, C=1, type=NULL)
	epoc.svdplot(G.svd, C=1)
  ## S3 method for class 'EPoC.survival'
plot(x,...)
  ## S3 method for class 'EPoC.survival'
summary(object,...)
  ## S3 method for class 'summary.EPoC.survival'
print(x,...)
epoc.svd(model, k=1, C=1, numload=NULL)
  epoc.survival(G.svd, Y, U, surv, C=1, type=NULL)
	epoc.svdplot(G.svd, C=1)
  ## S3 method for class 'EPoC.survival'
plot(x,...)
  ## S3 method for class 'EPoC.survival'
summary(object,...)
  ## S3 method for class 'summary.EPoC.survival'
print(x,...)

Arguments

`model`	An object from `epocG` or `epocA` or a `Matrix` from `epoc.bootstrap` and friends.
`k`	In case `model` come from `epocG` or `epocA` select a model of sparsity level k in [1,K]. The default `k=1` means first/most sparse.
`C`	Default 1. For `epoc.svd` the number of components. For `epoc.survival` and `epoc.svdplot`, which component to use.
`numload`	Number of loadings in the sparse components, a vector for each component. Default 10 for all components.
`G.svd`	The list obtained from `epoc.svd`.
`Y`	mRNA, samples x genes.
`U`	CNA, samples x genes.
`surv`	Survival data for the samples.
`type`	`'G'` means EPoC G and `'A'` means EPoC A.
`x`	An object from `epoc.survival`
`object`	An object from `epoc.survival`
`...`	Parameters passed down to underlying functions, `plot.default` for `plot` and `print.default` for `print`.

Details

Applies survival analysis using the first SVD component, but other components can also be used by changing the input value of C. Survival scores are generated as described in Subsect. 2.4 in the second paper referenced. A simple non-parametric survival analysis is performed, comparing survival between patientswith positive or negative scores (tumor fitness).

Value

The epoc.survival object contains the summary information from a log-rank test comparing survival (survdiff) and survival fit objects.

References

Tobias Abenius, Rebecka Jörnsten, Teresia Kling, Linnéa Schmidt, José Sánchez, Sven Nelander. (2012) System-scale network modeling of cancer using EPoC. Advances in experimental medicine and biology

epoc.validation

Description

Model validation using random split or cross-validation

Usage

epoc.validation(type=c('pred','concordance'),repl,Y,U,lambdas=NULL,
   method='G',thr=1e-10,trace=0,...)epoc.validation(type=c('pred','concordance'),repl,Y,U,lambdas=NULL,
   method='G',thr=1e-10,trace=0,...)

Arguments

`type`	`'pred'` for 10-fold CV of prediction error. `'concordance'` for random split network concordance using Kendall $W$ .
`repl`	The number of replicates
`Y`	mRNA, samples x genes
`U`	CNA, samples x genes
`lambdas`	series of relative $\lambda$ s or default=`NULL` which means let EPoC choose
`method`	`'G'` means EPoC G and `'A'` means EPoC A.
`thr`	Threshold for convergence to the LASSO solver
`trace`	Debug information
`...`	Extra parameters passed through to the EPoC solver

Details

In the case of 'pred' assess CV prediction error using 10-fold cross-validation with repl replicates. In the case of 'concordance' assess network concordance using random split and Kendall W with repl replicates.

Value

A list of class plot.EPoC.validation.pred or plot.EPoC.validation.W respectively.

References

Plot BIC, Mallow's Cp and $\lambda$

Description

Plot BIC, Mallow's Cp and $\lambda$

Usage

modelselPlot(x, layout=NULL, k=1, showtitle=F, bthr=0, 
             showself=F, type=c('graph','modelsel'), ...)
## S3 method for class 'EPOCA'
plot(x, layout=NULL, k=1, showtitle=F, bthr=0, 
             showself=F, type=c('graph','modelsel'), ...)
## S3 method for class 'EPOCG'
plot(x, layout=NULL, k=1, showtitle=F, bthr=0, 
             showself=F, type=c('graph','modelsel'), ...)
modelselPlot(x, layout=NULL, k=1, showtitle=F, bthr=0, 
             showself=F, type=c('graph','modelsel'), ...)
## S3 method for class 'EPOCA'
plot(x, layout=NULL, k=1, showtitle=F, bthr=0, 
             showself=F, type=c('graph','modelsel'), ...)
## S3 method for class 'EPOCG'
plot(x, layout=NULL, k=1, showtitle=F, bthr=0, 
             showself=F, type=c('graph','modelsel'), ...)

Arguments

`x`	An EPoC G or EPoC A object
`layout`	Not used only for `type='modelsel'`
`k`	Not used for `type='modelsel'`
`showtitle`	Not used for `type='modelsel'`
`bthr`	Not used for `type='modelsel'`
`showself`	Not used for `type='modelsel'`
`type`	This page documents `type='modelsel'` only, for `type='graph'` see plot.EPOCG and epoc.bootplot
`...`	Parameters passed down to underlying functions, `plot`, `lines`, `points`, `abline`, `axis`, `text` and `legend`.

Details

Creates a plot that aids in model selection. Scale Bayesian information criterion (BIC) and Mallow's $C_p$ between zero on one and put that on the y-axis and put relative $\lambda$ values on the x-axis.

References

Parallell list apply

Description

Parallell list apply

Usage

plapply(X1, X2, FUN, ...)
plapply(X1, X2, FUN, ...)

Arguments

`X1`	a vector (atomic or list). Other objects (including classed objects) will be coerced by `as.list`.
`X2`	See `X1`.
`FUN`	the function to be applied to each pair of `X1` and `X2` at the corresponding positions.
`...`	optional arguments to `FUN`.

Details

FUN is found by a call to match.fun and typically is specified as a function or a symbol (e.g. a backquoted name) or a character string specifyign a function to be searched for from the environment of the call to plapply.

Function FUN must be able to accept as input any of the element pairs of X1 and X2. If any of these are atomic vectors, FUN will always be passed a length-one vector of the same type as X1, X2 respectively.

Users of S4 classes should pass a list to plapply: the internal coercion is done by the system as.list in the base namespace and not one defined by a user (e.g. by setting S4 methods on the system function).

Value

A list.

Examples

X1 <- array(1:4,dim=c(2,2))
X2 <- array(5:8,dim=c(2,2))
X3 <- array(9:12,dim=c(2,2))
X4 <- array(13:16,dim=c(2,2))
l <- plapply(list(X1,X2),list(X3,X4), function(E1,E2) E2 - E1)
stopifnot(all(sapply(l,sum)/4 == 4*2))
X1 <- array(1:4,dim=c(2,2))
X2 <- array(5:8,dim=c(2,2))
X3 <- array(9:12,dim=c(2,2))
X4 <- array(13:16,dim=c(2,2))
l <- plapply(list(X1,X2),list(X3,X4), function(E1,E2) E2 - E1)
stopifnot(all(sapply(l,sum)/4 == 4*2))

Plot model validation criteria

Description

Plot model validation criteria

Usage

  ## S3 method for class 'EPoC.validation.W'
plot(x, ...)
  ## S3 method for class 'EPoC.validation.pred'
plot(x, ...)
## S3 method for class 'EPoC.validation.W'
plot(x, ...)
  ## S3 method for class 'EPoC.validation.pred'
plot(x, ...)

Arguments

`x`	An object of type `EPoC.validation.W` or `EPoC.validation.W` respectively.
`...`	Parameters passed down to underlying functions, `plot`, `lines`, `points`, `abline`.

Details

Plot Kendall W or prediction error, respectively on the y-axis, network size on the upper x-axis and $\lambda$ on the lower x-axis. The plot fit a loess model of degree 1 to the points from the input object and finds the largest network size and corresponding $\lambda$ such that W is maximized or prediction error is minimized, respectively.

References

Blinded cancer mRNA, CNA and survival data

Description

This dataset contains blinded mRNA, CNA and survival data of 186 cancer tumors modified for demonstration usage. Some genes are randomly selected from 10672 probes, others are chosen for their characteristics.

mRNA is standardized to sd=1 and mean=0. CNA is centered to mean=0. survival is in days.

Usage

data(synth)data(synth)

Format

The synth data set is a list containing mRNA y, CNA u and surv survival data.

Examples

## Not run:   
  data(synth)
  y <- synth$y
  # standardize u
  u <- apply(synth$u, 2, function(x) x/sd(x))
  G <- epocG(Y=y, U=u)
  summary(G)
  plot(G)

## End(Not run)
## Not run:   
  data(synth)
  y <- synth$y
  # standardize u
  u <- apply(synth$u, 2, function(x) x/sd(x))
  G <- epocG(Y=y, U=u)
  summary(G)
  plot(G)

## End(Not run)

Package 'epoc'

Help Index

EPoC

Description

Usage

Arguments

Details

Value

Note

Author(s)

References

See Also

Examples

epoc.bootstrap

Description

Usage

Arguments

Details

Value

References

See Also

epoc.survival

Description

Usage

Arguments

Details

Value

References

See Also

epoc.validation

Description

Usage

Arguments

Details

Value

References

See Also

Plot BIC, Mallow's Cp and λ\lambdaλ

Description

Usage

Arguments

Details

References

See Also

Parallell list apply

Description

Usage

Arguments

Details

Value

See Also

Examples

Plot model validation criteria

Description

Usage

Arguments

Details

References

See Also

Blinded cancer mRNA, CNA and survival data

Description

Usage

Format

Examples

Plot BIC, Mallow's Cp and $\lambda$