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 
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, lattice, 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

R topics documented:
Cascade-package . .
analyze_network . .
as.micro_array . . .
compare . . . . . . .
cutoff . . . . . . . .
dim . . . . . . . . .
evolution . . . . . .
geneNeighborhood .
genePeakSelection .
gene_expr_simulation
head . . . . . . . . .
inference . . . . . .
micropredict-class . .
micro_array-class . .

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2

Cascade-package
micro_S . . . . . . .
micro_US . . . . . .
network . . . . . . .
network-class . . . .
network_random . .
plot-methods . . . .
position-methods . .
predict . . . . . . . .
print-methods . . . .
summary-methods . .
unionMicro-methods

Cascade-package

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The Cascade Package

Description
The Cascade is a modeling tool allowing gene selection, reverse engineering, and prediction.
Details
Package:
Type:
Version:
Date:
License:
Depends:

Cascade
Package
1.03
2014-03-24
GNU 2.0
methods

Author(s)
This package has been written by Nicolas Jung in collaboration with Frederic Bertrand, Myriam
Maumy-Bertrand and Laurent Vallat. Maintainer: 
References
Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2013). Cascade: a Rpackage 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 Rpackage 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)

4

as.micro_array

Coerce a matrix into a micro_array object.

as.micro_array

Description
Coerce a matrix into a micro_array object.
Usage
as.micro_array(M, time, subject)
Arguments
M

A 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 Rpackage 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

Some basic criteria of comparison between actual and inferred network.

compare

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 Rpackage 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,...)

6

dim

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 Rpackage 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

evolution

7

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 Rpackage 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)

8

geneNeighborhood

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 Rpackage 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
x

either 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.

y

either 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 specification is used when the overall is to compare two conditions. The condition&time specification is used when comparing two conditions at two precise 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 pattern 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 diffenrtially genes that will be selected. If 0
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