EPoC {epoc} | R Documentation |
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, ...)
Arguments
Y |
N x p matrix of mRNA transcript levels for p genes and N samples for epocA and epocG. For |
U |
N x p matrix of DNA copy number |
lambdas |
Non-negative vector of relative regularization parameters for lasso. |
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 |
predictorix |
For |
getall |
Logical. For |
file |
either a character string naming a file or a connection open for writing. |
append |
logical. Only relevant if |
model |
Model set from epocA or epocG |
k |
Select a model of sparsity level k in [1,K]. In |
newdata |
List of Y and U matrices required for prediction. |
x |
Model parameter to |
object |
Model parameter to |
... |
Parameters passed down to underlying function, e.g. |
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 |
links |
the number of links for the different |
lambdas |
the |
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)
See Also
print
, modelselPlot
,
epoc.validation
,
epoc.bootstrap
,
plot.EPoC.validation.pred
,
plot.EPoC.validation.W
,
coef
, predict
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)