Cascade Manual
Cascade-manual
Cascade-manual
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Package ‘Cascade’
March 24, 2014
Type Package
Title Selection, Reverse-Engineering and Prediction in Cascade networks.
Version 1.03
Date 2014-03-24
Author Nicolas Jung, Frederic Bertrand, Myriam Maumy-Bertrand Laurent Vallat
Maintainer <cascade@math.unistra.fr>
Description
The Cascade is a modeling tool allowing gene selection, reverse engineering, and prediction.
License GPL (>= 2)
Depends methods, abind, animation, cluster, graphics, grDevices, grid,igraph, lars, lat-
tice, limma, magic, methods, nnls, splines,stats, stats4, survival, tnet, utils, VGAM
Collate global.R micro_array.R network.R micro_array-network.R micropredict.R
Rtopics documented:
Cascade-package ...................................... 1
analyze_network ...................................... 2
as.micro_array ....................................... 3
compare........................................... 4
cutoff ............................................ 5
dim ............................................. 6
evolution .......................................... 6
geneNeighborhood ..................................... 7
genePeakSelection ..................................... 8
gene_expr_simulation.................................... 9
head............................................. 10
inference .......................................... 10
micropredict-class...................................... 11
micro_array-class...................................... 12
1
2Cascade-package
micro_S........................................... 13
micro_US.......................................... 13
network ........................................... 14
network-class ........................................ 14
network_random ...................................... 15
plot-methods ........................................ 16
position-methods ...................................... 17
predict............................................ 17
print-methods........................................ 17
summary-methods...................................... 18
unionMicro-methods .................................... 18
Cascade-package The Cascade Package
Description
The Cascade is a modeling tool allowing gene selection, reverse engineering, and prediction.
Details
Package: Cascade
Type: Package
Version: 1.03
Date: 2014-03-24
License: GNU 2.0
Depends: methods
Author(s)
This package has been written by Nicolas Jung in collaboration with Frederic Bertrand, Myriam
Maumy-Bertrand and Laurent Vallat. Maintainer: <cascade@math.unistra.fr>
References
Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2013). Cascade: a R-
package to study, predict and simulate the diffusion of a signal through a temporal gene network.
Bioinformatics, btt705.
Vallat, L., Kemper, C. A., Jung, N., Maumy-Bertrand, M., Bertrand, F., Meyer, N., ... & Bahram,
S. (2013). Reverse-engineering the genetic circuitry of a cancer cell with predicted intervention in
chronic lymphocytic leukemia. Proceedings of the National Academy of Sciences, 110(2), 459-464.
analyze_network 3
analyze_network Analysing the network
Description
Calculates some indicators for each node in the network.
Usage
analyze_network(Omega,nv,...)
Arguments
Omega a network object
nv the level of cutoff at which the analysis should be done
... label_v : (optionnal) name of the genes
Value
A matrix containing, for each node, its betweenness,its degree, its output, its closeness.
Author(s)
Nicolas Jung, Fr\’ed\’eric Bertrand , Myriam Maumy-Bertrand.
References
Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2013). Cascade: a R-
package to study, predict and simulate the diffusion of a signal through a temporal gene network.
Bioinformatics, btt705.
Vallat, L., Kemper, C. A., Jung, N., Maumy-Bertrand, M., Bertrand, F., Meyer, N., ... & Bahram,
S. (2013). Reverse-engineering the genetic circuitry of a cancer cell with predicted intervention in
chronic lymphocytic leukemia. Proceedings of the National Academy of Sciences, 110(2), 459-464.
Examples
data(network)
analyze_network(network,nv=0)
4as.micro_array
as.micro_array Coerce a matrix into a micro_array object.
Description
Coerce a matrix into a micro_array object.
Usage
as.micro_array(M, time, subject)
Arguments
MA matrix. Contains the microarray measurements. Should of size N * K, with
N the number of genes and K=T*P with T the number of time points, and P the
number of individuals. This matrix should be created using cbind(M1,M2,...)
with M1 a N*T matrix with the measurements for individual 1, M2 a N*T matrix
with the measurements for individual 2.
time A vector. The time points measurements.
subject The number of subjects.
Value
A micro_array object.
Author(s)
Nicolas Jung, Fr\’ed\’eric Bertrand , Myriam Maumy-Bertrand.
References
Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2013). Cascade: a R-
package to study, predict and simulate the diffusion of a signal through a temporal gene network.
Bioinformatics, btt705.
Vallat, L., Kemper, C. A., Jung, N., Maumy-Bertrand, M., Bertrand, F., Meyer, N., ... & Bahram,
S. (2013). Reverse-engineering the genetic circuitry of a cancer cell with predicted intervention in
chronic lymphocytic leukemia. Proceedings of the National Academy of Sciences, 110(2), 459-464.
Examples
data(micro_US)
micro_US<-as.micro_array(micro_US,time=c(60,90,210,390),subject=6)
compare 5
compare Some basic criteria of comparison between actual and inferred net-
work.
Description
Allows comparison between actual and inferred network.
Usage
compare(Net, Net_inf, nv)
Arguments
Net A network object containing the actual network.
Net_inf A network object containing the inferred network.
nv A number that indicates at which level of cutoff the comparison should be done.
Value
A vector containing : sensibility, predictive positive value, and the F-score
Author(s)
Nicolas Jung, Fr\’ed\’eric Bertrand , Myriam Maumy-Bertrand.
References
Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2013). Cascade: a R-
package to study, predict and simulate the diffusion of a signal through a temporal gene network.
Bioinformatics, btt705.
Vallat, L., Kemper, C. A., Jung, N., Maumy-Bertrand, M., Bertrand, F., Meyer, N., ... & Bahram,
S. (2013). Reverse-engineering the genetic circuitry of a cancer cell with predicted intervention in
chronic lymphocytic leukemia. Proceedings of the National Academy of Sciences, 110(2), 459-464.
cutoff Choose the best cutoff
Description
Allows estimating the best cutoff, in function of the scale-freeness of the network. For a sequence
of cutoff, the corresponding p-value is then calculated.
Usage
cutoff(Omega,...)
6dim
Arguments
Omega a network object
... Optional arguments:
sequence a vector corresponding to the sequence of cutoffs that will be tested.
x_min an integer ; only values over x_min are further retained for performing
the test.
Value
A list containing two objects :
p.value the p values corresponding to the sequence of cutoff
p.value.inter
the smoothed p value vector, using the loess function
Author(s)
Nicolas Jung, Fr\’ed\’eric Bertrand , Myriam Maumy-Bertrand.
References
Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2013). Cascade: a R-
package to study, predict and simulate the diffusion of a signal through a temporal gene network.
Bioinformatics, btt705.
Vallat, L., Kemper, C. A., Jung, N., Maumy-Bertrand, M., Bertrand, F., Meyer, N., ... & Bahram,
S. (2013). Reverse-engineering the genetic circuitry of a cancer cell with predicted intervention in
chronic lymphocytic leukemia. Proceedings of the National Academy of Sciences, 110(2), 459-464.
Examples
data(network)
cutoff(network)
#See vignette for more details
dim Dimension of the data
Description
Dimension of the data
Methods
signature(x = "micro_array") Gives the dimension of the matrix of measurements.
evolution 7
evolution See the evolution of the network with change of cutoff
Description
See the evolution of the network with change of cutoff. This function may be usefull to see if the
global topology is changed while increasing the cutoff.
Usage
evolution(net,list_nv,...)
Arguments
net a network object
list_nv a vector of cutoff at which the network should be shown
... Optionnal arguments:
gr a vector giving the group of each gene
color.vertex a vector giving the color of each node
fix logical, should the position of the node in the network be calculated once at
the beginning ? Defaut to TRUE.
taille vector giving the size of the plot. Default to c(2000,1000)
... see plot function
Value
A HTML page with the evolution of the network.
Author(s)
Nicolas Jung, Fr\’ed\’eric Bertrand , Myriam Maumy-Bertrand.
References
Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2013). Cascade: a R-
package to study, predict and simulate the diffusion of a signal through a temporal gene network.
Bioinformatics, btt705.
Vallat, L., Kemper, C. A., Jung, N., Maumy-Bertrand, M., Bertrand, F., Meyer, N., ... & Bahram,
S. (2013). Reverse-engineering the genetic circuitry of a cancer cell with predicted intervention in
chronic lymphocytic leukemia. Proceedings of the National Academy of Sciences, 110(2), 459-464.
Examples
data(network)
sequence<-seq(0,0.2,length.out=20)
evolution(network,sequence)
8geneNeighborhood
geneNeighborhood Find the neighborhood of a set of nodes.
Description
Find the neighborhood of a set of nodes.
Usage
geneNeighborhood(net,targets,...)
Arguments
net a network object
targets a vector containing the set of nodes
... Optional arguments. See plot options.
Value
The neighborhood of the targeted genes.
Author(s)
Nicolas Jung, Fr\’ed\’eric Bertrand , Myriam Maumy-Bertrand.
References
Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2013). Cascade: a R-
package to study, predict and simulate the diffusion of a signal through a temporal gene network.
Bioinformatics, btt705.
Vallat, L., Kemper, C. A., Jung, N., Maumy-Bertrand, M., Bertrand, F., Meyer, N., ... & Bahram,
S. (2013). Reverse-engineering the genetic circuitry of a cancer cell with predicted intervention in
chronic lymphocytic leukemia. Proceedings of the National Academy of Sciences, 110(2), 459-464.
Examples
#See vignette
genePeakSelection 9
genePeakSelection Methods for selecting genes
Description
Selection of differentially expressed genes.
Usage
geneSelection(x,y,tot.number,...)
genePeakSelection(x,pic,...)
Arguments
xeither a micro_array object or a list of micro_array objects. In the first case, the
micro_array object represents the stimulated measurements. In the second case,
the control unstimulated data (if present) should be the first element of the list.
yeither a micro_array object or a list of strings. In the first case, the micro_array
object represents the stimulated measurements. In the second case, the list is the
way to specify the contrast:
First element: condition, condition&time or pattern. The condition specifica-
tion is used when the overall is to compare two conditions. The condi-
tion&time specification is used when comparing two conditions at two pre-
cise time points. The pattern specification allows to decide which time point
should be differentially expressed.
Second element: a vector of length 2. The two conditions which should be
compared. If a condition is used as control, it should be the first element
of the vector. However, if this control is not measured throught time, the
option cont=TRUE should be used.
Third element: depends on the first element. It is no needed if condition has
been specified. If condition&time has been specified, then this is a vector
containing the time point at which the comparison should be done. If pat-
tern has been specified, then this is a vector of 0 and 1 of length T, where
T is the number of time points. The time points with desired differential
expression are provided with 1.
tot.number an integer. The number of selected genes. If tot.number <0 all differentially
genes are selected. If tot.number > 1, tot.number is the maximum of diffenr-
tially genes that will be selected. If 0<tot.number<1, tot.number represents the
proportion of diffenrentially genes that are selected.
pic interger. At which time points measurements should the genes be selected [op-
tionnal for geneSelection].
... Optional arguments:
M2 a micro_array object. The unstimulated measurements.
data_log logical (default to TRUE) ; should data be logged ?
wanted.patterns a matrix with wanted patterns [only for geneSelection].
10 gene_expr_simulation
forbidden.patterns a matrix with forbidden patterns [only for geneSelection].
durPic vector of size 2 (default to c(1,1)) ; the first elements gives the length of
the pic at the left, the second at the right. [only for genePeakSelection]
abs_val logical (default to TRUE) ; should genes be selected on the basis of
their absolute value expression ? [only for genePeakSelection]
alpha_diff float ; the risk level
Value
A micro_array object.
Author(s)
Nicolas Jung, Fr\’ed\’eric Bertrand , Myriam Maumy-Bertrand.
References
Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2013). Cascade: a R-
package to study, predict and simulate the diffusion of a signal through a temporal gene network.
Bioinformatics, btt705.
Vallat, L., Kemper, C. A., Jung, N., Maumy-Bertrand, M., Bertrand, F., Meyer, N., ... & Bahram,
S. (2013). Reverse-engineering the genetic circuitry of a cancer cell with predicted intervention in
chronic lymphocytic leukemia. Proceedings of the National Academy of Sciences, 110(2), 459-464.
gene_expr_simulation
Simulates microarray data based on a given network.
Description
Simulates microarray data based on a given network.
Usage
gene_expr_simulation(network,...)
Arguments
network A network object.
... time_label a vector containing the time labels.
subject the number of subjects
level_pic the mean level of pics.
Value
A micro_array object.
head 11
Author(s)
Nicolas Jung, Fr\’ed\’eric Bertrand , Myriam Maumy-Bertrand.
References
Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2013). Cascade: a R-
package to study, predict and simulate the diffusion of a signal through a temporal gene network.
Bioinformatics, btt705.
Vallat, L., Kemper, C. A., Jung, N., Maumy-Bertrand, M., Bertrand, F., Meyer, N., ... & Bahram,
S. (2013). Reverse-engineering the genetic circuitry of a cancer cell with predicted intervention in
chronic lymphocytic leukemia. Proceedings of the National Academy of Sciences, 110(2), 459-464.
Examples
#See vignette
head Overview of a micro_array object
Description
Overview of a micro_array object.
Methods
signature(x = "ANY") Gives an overview.
signature(x = "micro_array") Gives an overview.
inference Reverse-engineer the network
Description
Reverse-engineer the network.
Usage
inference(M,...)
12 micropredict-class
Arguments
Ma micro_array object.
... Optional arguments:
tour.max=30 maximal number of steps.
g=function(x) 1/x the new solution is choosen as (the old solution + g(x) * the
new solution)/(1+g(x)) where x is the number of steps.
conv=10e-3 convergence criterion.
cv.subjects=TRUE should the cross validation be done removing the subject
one by one ?
nb.folds=NULL Relevant only if cv.subjects is FALSE. The number of folds in
cross validation.
eps=10e-5 machine zero
type.inf="iterative" "iterative" or "noniterative" : should the algorithm be com-
puted iteratively
Value
A network object.
Author(s)
Nicolas Jung, Fr\’ed\’eric Bertrand , Myriam Maumy-Bertrand.
References
Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2013). Cascade: a R-
package to study, predict and simulate the diffusion of a signal through a temporal gene network.
Bioinformatics, btt705.
Vallat, L., Kemper, C. A., Jung, N., Maumy-Bertrand, M., Bertrand, F., Meyer, N., ... & Bahram,
S. (2013). Reverse-engineering the genetic circuitry of a cancer cell with predicted intervention in
chronic lymphocytic leukemia. Proceedings of the National Academy of Sciences, 110(2), 459-464.
Examples
#data(micro_US)
#inference(micro_US)
#See vignette for more details
micropredict-class Class "micropred"
Description
2254
micro_array-class 13
Objects from the Class
Objects can be created by calls of the form new("micropred", ...).
Examples
showClass("network")
micro_array-class Class "micro_array"
Description
The Class
Objects from the Class
Objects can be created by calls of the form new("micro_array", ...).
Slots
microarray:Object of class "matrix" ~~
name:Object of class "vector" ~~
group:Object of class "vector" ~~
start_time:Object of class "vector" ~~
time:Object of class "vector" ~~
subject:Object of class "numeric" ~~
Methods
dim signature(x = "micro_array"): ...
genePicSelection signature(M1 = "micro_array", M2 = "micro_array", pic = "numeric"):
...
geneSelection signature(x = "micro_array", y = "micro_array", tot.number = "numeric"):
...
geneSelection signature(x = "list", y = "list", tot.number = "numeric"):
...
head signature(x = "micro_array"): ...
inference signature(M = "micro_array"): ...
plot signature(x = "micro_array", y = "ANY"): ...
plot signature(x = "micro_array", y = "ANY"): ...
plot signature(x = "micropredict", y = "ANY"): ...
predict signature(object = "micro_array"): ...
print signature(x = "micro_array"): ...
summary signature(object = "micro_array"): ...
unionMicro signature(M1 = "micro_array", M2 = "micro_array"): ...
14 micro_US
Examples
showClass("micro_array")
micro_S Stimulated data
Description
Stimulated data
Usage
data(micro_S)
Format
A data frame with 54613 probesets measured 6 times throught 4 time points.
References
Vallat, L., Park, Y., Li, C., & Gribben, J. G. (2007). Temporal genetic program following B-cell
receptor cross-linking: altered balance between proliferation and death in healthy and malignant
B cells. Blood, 109(9), 3989-3997.\ Vallat, L., Kemper, C. A., Jung, N., Maumy-Bertrand, M.,
Bertrand, F., Meyer, N., ... & Bahram, S. (2013). Reverse-engineering the genetic circuitry of
a cancer cell with predicted intervention in chronic lymphocytic leukemia. Proceedings of the
National Academy of Sciences, 110(2), 459-464.
Examples
data(micro_S)
micro_US Unstimulated data
Description
Unstimulated data
Usage
data(micro_US)
Format
A data frame with 54613 probesets measured 6 times throught 4 time points.
network 15
References
Vallat, L., Park, Y., Li, C., & Gribben, J. G. (2007). Temporal genetic program following B-cell
receptor cross-linking: altered balance between proliferation and death in healthy and malignant
B cells. Blood, 109(9), 3989-3997.\ Vallat, L., Kemper, C. A., Jung, N., Maumy-Bertrand, M.,
Bertrand, F., Meyer, N., ... & Bahram, S. (2013). Reverse-engineering the genetic circuitry of
a cancer cell with predicted intervention in chronic lymphocytic leukemia. Proceedings of the
National Academy of Sciences, 110(2), 459-464.
Examples
data(micro_US)
network A network object data.
Description
A network object (the same as the result in the vignette).
Usage
data(network)
Examples
data(network)
plot(network)
print(network)
network-class Class "network"
Description
2254
Objects from the Class
Objects can be created by calls of the form new("network", ...).
16 network_random
Slots
network:Object of class "matrix" ~~
name:Object of class "vector" ~~
F:Object of class "array" ~~
convF:Object of class "matrix" ~~
convO:Object of class "vector" ~~
time_pt:Object of class "vector" ~~
Methods
analyze_network signature(Omega = "network"): ...
cutoff signature(Omega = "network"): ...
evolution signature(net = "network"): ...
geneNeighborhood signature(net = "network"): ...
plot signature(x = "network", y = "ANY"): ...
plot signature(x = "network", y = "micro_array"): ...
position signature(net = "network"): ...
print signature(x = "network"): ...
Examples
showClass("network")
network_random Generates a network.
Description
Generates a network.
Usage
network_random(nb, time_label, exp, init, regul, min_expr, max_expr, casc.level)
Arguments
nb Integer. The number of genes.
time_label Vector. The time points measurements.
exp The exponential parameter, as in the barabasi.game function in igraph package.
init The attractiveness of the vertices with no adjacent edges. See barabasi.game
function.
regul A vector mapping each gene with its number of regulators.
min_expr Minimum of strength of a non-zero link
max_expr Maximum of strength of a non-zero link
casc.level ...
plot-methods 17
Value
A network object.
Author(s)
Nicolas Jung, Fr\’ed\’eric Bertrand , Myriam Maumy-Bertrand.
References
Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2013). Cascade: a R-
package to study, predict and simulate the diffusion of a signal through a temporal gene network.
Bioinformatics, btt705.
Vallat, L., Kemper, C. A., Jung, N., Maumy-Bertrand, M., Bertrand, F., Meyer, N., ... & Bahram,
S. (2013). Reverse-engineering the genetic circuitry of a cancer cell with predicted intervention in
chronic lymphocytic leukemia. Proceedings of the National Academy of Sciences, 110(2), 459-464.
plot-methods Plot
Description
Considering the class of the argument which is passed to plot, the graphical output differs.
Methods
signature(x = "micro_array", y = "ANY",...) xa micro\_array object
list_nv a vector of cutoff at which the network should be shown
signature(x = "network", y = "ANY",...) xa network object
... Optionnal arguments:
gr a vector giving the group of each gene
choice what graphic should be plotted: either "F" (for a representation of the matrices F)
or "network".
nv the level of cutoff. Defaut to 0.
ini using the “position” function, you can fix the position of the nodes
color.vertex a vector defining the color of the vertex
ani vector giving the size of the plot. Default to c(2000,1000)
video if ani is TRUE and video is TRUE, the animation result is a GIF video
label_v vector defining the vertex labels
legend.position position of the legend
frame.color color of the frames
label.hub logical ; if TRUE only the hubs are labeled
edge.arrow.size size of the arrows ; default to 0.7
edge.thickness edge thickness ; default to 1.
signature(x = "micropredict", y = "ANY",...) xa micropredict object
... Optionnal arguments: see plot for network
18 print-methods
position-methods Returns the position of edges in the network
Description
Returns the position of edges in the network
Methods
signature(net = "network") Returns a matrix with the position of the node. This matrix
can then be used as an argument in the plot function.
predict Prediction of the gene expressions after a knock-out experience
predict
Description
Prediction of the gene expressions after a knock-out experience
Usage
predict(object,...)
Arguments
object a micro_array object
... Other arguments:
Omega a netowork object.
nv [=0] numeric ; the level of the cutoff
targets [NULL] vector ; which genes are knocked out ?
Author(s)
Nicolas Jung, Fr\’ed\’eric Bertrand , Myriam Maumy-Bertrand.
print-methods ~~ Methods for Function print ~~
Description
Methods for function print ~~
summary-methods 19
summary-methods Methods for Function summary
Description
Methods for function summary
unionMicro-methods Makes the union between two micro_array objects.
Description
Makes the union between two micro_array objects.
Methods
signature(M1 = "micro_array", M2 = "micro_array") Returns a micro_array ob-
ject which is the union of M1 and M2.
signature(M1 = "list", M2 = "ANY") Returns a micro_array object which is the union
of the elements of M1.
