Type:	Package
Title:	Deciphering Biological Networks with Patterned Heterogeneous Measurements
Version:	1.6
Date:	2025-07-02
Depends:	R (≥ 3.5.0)
Imports:	abind, cluster, e1071, gplots, graphics, grDevices, igraph, jetset, lars, lattice, limma, methods, Mfuzz, movMF, nnls, plotrix, SelectBoost, tnet, VGAM, WGCNA
Suggests:	animation, Biobase, biomaRt, c060, CascadeData, elasticnet, glmnet, knitr, pixmap, R.rsp, rmarkdown, spls
Author:	Frederic Bertrand [cre, aut], Myriam Maumy-Bertrand [aut]
Maintainer:	Frederic Bertrand <frederic.bertrand@utt.fr>
Description:	A modeling tool dedicated to biological network modeling (Bertrand and others 2020, <doi:10.1093/bioinformatics/btaa855>). It allows for single or joint modeling of, for instance, genes and proteins. It starts with the selection of the actors that will be the used in the reverse engineering upcoming step. An actor can be included in that selection based on its differential measurement (for instance gene expression or protein abundance) or on its time course profile. Wrappers for actors clustering functions and cluster analysis are provided. It also allows reverse engineering of biological networks taking into account the observed time course patterns of the actors. Many inference functions are provided and dedicated to get specific features for the inferred network such as sparsity, robust links, high confidence links or stable through resampling links. Some simulation and prediction tools are also available for cascade networks (Jung and others 2014, <doi:10.1093/bioinformatics/btt705>). Example of use with microarray or RNA-Seq data are provided.
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Encoding:	UTF-8
Collate:	Patterns-package.R global.R datasets.R omics_array.R omics_network.R omics_array-omics_network.R omics_predict.R
Classification/MSC:	62J05, 62J07, 62J99, 92C42
VignetteBuilder:	knitr
RoxygenNote:	7.3.2
URL:	https://fbertran.github.io/Patterns/, https://github.com/fbertran/Patterns/
BugReports:	https://github.com/fbertran/Patterns/issues/
NeedsCompilation:	no
Packaged:	2025-07-02 15:21:59 UTC; bertran7
Repository:	CRAN
Date/Publication:	2025-07-02 16:50:12 UTC

Patterns: Deciphering Biological Networks with Patterned Heterogeneous Measurements

Description

A modeling tool dedicated to biological network modeling (Bertrand and others 2020, doi:10.1093/bioinformatics/btaa855). It allows for single or joint modeling of, for instance, genes and proteins. It starts with the selection of the actors that will be the used in the reverse engineering upcoming step. An actor can be included in that selection based on its differential measurement (for instance gene expression or protein abundance) or on its time course profile. Wrappers for actors clustering functions and cluster analysis are provided. It also allows reverse engineering of biological networks taking into account the observed time course patterns of the actors. Many inference functions are provided and dedicated to get specific features for the inferred network such as sparsity, robust links, high confidence links or stable through resampling links. Some simulation and prediction tools are also available for cascade networks (Jung and others 2014, doi:10.1093/bioinformatics/btt705). Example of use with microarray or RNA-Seq data are provided.

Author(s)

Maintainer: Frederic Bertrand frederic.bertrand@utt.fr (ORCID)

Authors:

• Myriam Maumy-Bertrand myriam.maumy-bertrand@utt.fr (ORCID)

References

F. Bertrand, I. Aouadi, N. Jung, R. Carapito, L. Vallat, S. Bahram, M. Maumy-Bertrand (2020). SelectBoost: a general algorithm to enhance the performance of variable selection methods, Bioinformatics, doi:10.1093/bioinformatics/btaa855.

C. Schleiss, [...], M. Maumy-Bertrand, S. Bahram, F. Bertrand, and L. Vallat. (2021). Temporal multiomic modelling reveals a B-cell receptor proliferative program in chronic lymphocytic leukemia. Leukemia.

See Also

Useful links:

• https://fbertran.github.io/Patterns/

• https://github.com/fbertran/Patterns/

• Report bugs at https://github.com/fbertran/Patterns/issues/

Expression data from healthy and malignant (chronic lymphocytic leukemia, CLL) human B-lymphocytes after B-cell receptor stimulation (GSE 39411 dataset)

Description

B-cells were negatively selected from healthy donors and previously untreated CLL patients. BCR stimulated and unstimulated control B-cells were treated at four time points after stimulation for total RNA extraction and hybridization on Affymetrix microarrays.

Format

The format is: chr "CLL"

Details

The dataset provided with package is the first five lines of the full dataset. The full dataset can be downloaded from the github repository of the package (https://raw.githubusercontent.com/fbertran/Patterns/master/add_data/CLL.RData).

Three different cell populations (6 healthy B-lymphocytes, 6 leukemic CLL B-lymphocyte of indolent form and 5 leukemic CLL B-lymphocyte of aggressive form) were stimulated in vitro with an anti-IgM antibody, activating the B-cell receptor (BCR). We analyzed the gene expression at 4 time points (60, 90, 210 and 390 minutes). Each gene expression measurement is performed both in stimulated cells and in control unstimulated cells. For one aggressive CLL case, we silenced expression of DUSP1 by transfecting DUSP1-specific RNAi and, as a control, transfected cells with a non-targeting RNAi. We then stimulated the BCR of these cells and analyzed the gene expression at the same time points in stimulated cells and in control unstimulated cells.

Author(s)

Bertrand Frederic, Myriam Maumy-Bertrand.

Source

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE39411

References

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 of the United States of America, 110(2), 459–464.

Examples

data(CLL)
str(CLL)



CLLfile <- "https://github.com/fbertran/Patterns/raw/master/add_data/CLL.RData"
download.file(CLLfile,destfile = file.path(tempdir(),"downloadData.RData"),method = "libcurl")
load(file.path(tempdir(),"downloadData.RData"))
unlink(file.path(tempdir(),"downloadData.RData"))
str(CLL)

Create initial F matrices for cascade networks inference.

Description

This is an helper function to create initial values F matrices for cascade networks.

Usage

CascadeFinit(sqF, ngrp, low.trig = TRUE)

Arguments

Value

An array of sizes c(sqF, sqF, ngrp).

Author(s)

Bertrand Frederic, Myriam Maumy-Bertrand.

Examples

CascadeFinit(3,2)
CascadeFinit(4,3)
plotF(CascadeFinit(4,3),choice = "F")
CascadeFinit(3,2,low.trig=FALSE)
CascadeFinit(4,3,low.trig=FALSE)
plotF(CascadeFinit(4,3,low.trig=FALSE),choice = "F")

Create F matrices shaped for cascade networks inference.

Description

This is an helper function to create F matrices with special shape used for cascade networks.

Usage

CascadeFshape(sqF, ngrp)

Arguments

Value

An array of sizes c(sqF, sqF, ngrp).

Author(s)

Bertrand Frederic, Myriam Maumy-Bertrand.

Examples

CascadeFshape(3,2)
plotF(CascadeFshape(3,2),choice = "Fshape")
CascadeFshape(4,3)
plotF(CascadeFshape(4,3),choice = "Fshape")

Create initial F matrices using specific intergroup actions for network inference.

Description

This is an helper function to create initial values F matrices for networks.

Usage

IndicFinit(sqF, ngrp, Indic, low.trig = TRUE)

Arguments

Value

An array of size (sqF, sqF, ngrp).

Author(s)

Bertrand Frederic, Myriam Maumy-Bertrand.

Examples

IndicFinit(3, 2, matrix(1,2,2)-diag(2))

Create F matrices using specific intergroup actions for network inference.

Description

This is an helper function to create values F matrices using specific intergroup actions for network inference.

Usage

IndicFshape(sqF, ngrp, Indic)

Arguments

Value

An array of size (sqF, sqF, ngrp).

Author(s)

Bertrand Frederic, Myriam Maumy-Bertrand.

Examples

IndicFshape(3, 2, matrix(1,2,2)-diag(2))

Simulated microarray.

Description

Simulated M, microarray.

Author(s)

Bertrand Frederic, Myriam Maumy-Bertrand.

Examples

data(M)
head(M)
str(M)

Simulated network for examples.

Description

Simulated network.

Author(s)

Bertrand Frederic, Myriam Maumy-Bertrand.

Examples

data(Net)
str(Net)

Reverse-engineered network of the M and Net simulated data.

Description

The reverse-engineered network with the 'Patterns' package using the fitfun="LASSO" default function and a cascade network setting.

Author(s)

Bertrand Frederic, Myriam Maumy-Bertrand.

Examples

data(Net_inf_PL)
str(Net_inf_PL)

Selection of genes.

Description

20 (at most) genes with differential expression at t1, 20 (at most) genes with differential expression at t2, 20 (at most) genes with differential expression at t3, 20 (at most) genes with differential expression at t4 et 20 (at most) genes with global differential expression were selected.

Author(s)

Bertrand Frederic, Myriam Maumy-Bertrand.

Examples

data(Selection)
head(Selection)
summary(Selection,3)

Analysing the network

Description

Calculates some indicators for each node in the network.

Usage

## S4 method for signature 'omics_network'
analyze_network(Omega, nv, label_v = NULL)

Arguments

Value

A matrix containing, for each node, its betweenness,its degree, its output, its closeness.

Author(s)

Bertrand Frederic, Myriam Maumy-Bertrand.

Examples

data(network)
analyze_network(network,nv=0)

Coerce a matrix into a omics_array object.

Description

Coerce a matrix into a omics_array object.

Usage

as.omics_array(
  M,
  time,
  subject,
  name_probe = NULL,
  gene_ID = NULL,
  group = 0,
  start_time = 0
)

Arguments

Value

A omics_array object.

Author(s)

Bertrand Frederic, Myriam Maumy-Bertrand.

Examples

if(require(CascadeData)){
	data(micro_US, package="CascadeData")
	micro_US<-as.omics_array(micro_US[1:100,],time=c(60,90,210,390),subject=6)
	plot(micro_US)
	}

A function to explore a dataset and cluster its rows.

Description

Based on soft clustering performed by the Mfuzz package.

Usage

## S4 method for signature 'omics_array'
clustExploration(omicsarray, new.window = FALSE)

Arguments

Value

A data.frame of nrows(omicsarray) observations of 3 variables (name, cluster, maj.vote.index).

Author(s)

Bertrand Frederic, Myriam Maumy-Bertrand.

Examples

library(Patterns)
if(require(CascadeData)){
data(micro_S, package="CascadeData")
D<-Patterns::as.omics_array(micro_S[1:100,],1:4,6)
a<-clustExploration(D)
a
}

A function to explore a dataset and cluster its rows.

Description

Based on soft clustering performed by the Mfuzz package.

Usage

## S4 method for signature 'omics_array,numeric'
clustInference(omicsarray, vote.index, new.window = FALSE)

Arguments

Value

A list of two elements:

Author(s)

Bertrand Frederic, Myriam Maumy-Bertrand.

Examples

library(Patterns)
if(require(CascadeData)){
data(micro_S, package="CascadeData")
D<-Patterns::as.omics_array(micro_S[1:20,],1:4,6)
b<-Patterns::clustInference(D,0.5)
b
}

Some basic criteria of comparison between actual and inferred network.

Description

Allows comparison between actual and inferred network.

Usage

## S4 method for signature 'omics_network,omics_network,numeric'
compare(Net, Net_inf, nv = 1)

Arguments

Value

A vector containing : sensitivity, predictive positive value, the usual F-score (2*ppv*sens/(sppvpe+sens)), the 1/2 ponderated Fscore ((1+0.5^2)*ppv*sens/(ppv/4+sens)) and the 2 ponderated Fscore ((1+2^2)*ppv*sens/(ppv*4+sens)).

Methods

list("signature(Net = \"omics_network\", Net_inf = \"omics_network\", nv = \"numeric\")")

Net

A omics_network object containing the actual network.

Net_inf

A omics_network object containing the inferred network.

nv

A number that indicates at which level of cutoff the comparison should be done.

Author(s)

Bertrand Frederic, Myriam Maumy-Bertrand.

Examples

data(Net)
data(Net_inf_PL)

#Comparing true and inferred networks
Crit_values=NULL

#Here are the cutoff level tested
test.seq<-seq(0,max(abs(Net_inf_PL@omics_network*0.9)),length.out=200)
for(u in test.seq){
	Crit_values<-rbind(Crit_values,Patterns::compare(Net,Net_inf_PL,u))
}
matplot(test.seq,Crit_values,type="l",ylab="Criterion value",xlab="Cutoff level",lwd=2)
legend(x="topleft", legend=colnames(Crit_values), lty=1:5,col=1:5,ncol=2,cex=.9)

Choose the best cutoff

Description

Allows estimating the best cutoff. For a sequence of cutoff, the p value corresponding to each cutoff value of the sequence. Mainly recommended for single time cascade networks. To achieve more sparsity in other settings, please use a fiiting function based on the stability selection or selectboost algorithms.

Usage

## S4 method for signature 'omics_network'
cutoff(Omega, sequence = NULL, x_min = 0)

Arguments

Value

A list containing two objects :

Author(s)

Bertrand Frederic, Myriam Maumy-Bertrand.

Examples

		data(network)
		cutoff(network)
		#See vignette for more details

Dimension of the data

Description

Dimension of the data

Usage

## S4 method for signature 'omics_array'
dim(x)

Arguments

Methods

list("signature(x = \"omics_array\")")

Gives the dimension of the matrix of measurements.

Author(s)

Bertrand Frederic, Myriam Maumy-Bertrand.

Human transcription factors from HumanTFDB

Description

Retrieve human transcription factors from HumanTFDB, extracted from AnimalTFDB 3.0: a comprehensive resource for annotation and prediction of animal transcription factors. Hui Hu, Ya-Ru Miao, Long-Hao Jia, Qing-Yang Yu, Qiong Zhang and An-Yuan Guo. *Nucl. Acids Res*. (2018).

Author(s)

Bertrand Frederic, Myriam Maumy-Bertrand.

Examples

data(doc)
head(doc)
summary(doc)

See the evolution of the network with change of cutoff

Description

See the evolution of the network with change of cutoff

Usage

## S4 method for signature 'omics_network'
evolution(
  net,
  list_nv,
  gr = NULL,
  color.vertex = NULL,
  color.edge = NULL,
  fix = TRUE,
  size = c(2000, 1000),
  label_v = 1:dim(net@omics_network)[1],
  legend.position = "topleft",
  frame.color = "black",
  label.hub = FALSE,
  outdir,
  type.ani = "html"
)

Arguments

Details

Several types of outputs are available using the type.ani option.

• html

• latex (requires latex)

• swf (requires swftools)

• video (requires ffmpeg)

• gif

• manual_gif

Value

A HTML page with the evolution of the network.

Author(s)

Bertrand Frederic, Myriam Maumy-Bertrand.

Examples

data(network)
sequence<-seq(0,0.2,length.out=20)

#Change the destdir to have the animation created where you want. 
destdir = tempdir()

#Example of use of the evolution method with an html output.
evolution(network,sequence,type.ani = "html", outdir=destdir)


## Not run: 
#Example of use of the evolution method with an animated gif output.
evolution(network,sequence,type.ani = "gif", outdir=destdir)

## End(Not run)

Find the neighborhood of a set of nodes.

Description

Find the neighborhood of a set of nodes.

Usage

## S4 method for signature 'omics_network'
geneNeighborhood(
  net,
  targets,
  nv = 0,
  order = length(net@time_pt) - 1,
  label_v = NULL,
  ini = NULL,
  frame.color = "white",
  label.hub = FALSE,
  graph = TRUE,
  names = F
)

Arguments

Value

The neighborhood of the targeted genes.

Author(s)

Bertrand Frederic, Myriam Maumy-Bertrand.

Examples

data(Selection)
data(infos)
#Find probesets for EGR1
pbst_EGR1 = infos[infos$hgnc_symbol=="EGR1", "affy_hg_u133_plus_2"]

gene_IDs = infos[match(Selection@name, infos$affy_hg_u133_plus_), "hgnc_symbol"]

data(network)
#A nv value can chosen using the cutoff function
nv=.11 
EGR1<-which(is.element(Selection@name,pbst_EGR1))
P<-position(network,nv=nv)

geneNeighborhood(network,targets=EGR1,nv=nv,ini=P,
label_v=gene_IDs)
          

Methods for selecting genes

Description

Selection of differentially expressed genes.

Usage

## S4 method for signature 'omics_array,omics_array,numeric'
geneSelection(
  x,
  y,
  tot.number,
  data_log = TRUE,
  wanted.patterns = NULL,
  forbidden.patterns = NULL,
  peak = NULL,
  alpha = 0.05,
  Design = NULL,
  lfc = 0
)

## S4 method for signature 'list,list,numeric'
geneSelection(
  x,
  y,
  tot.number,
  data_log = TRUE,
  alpha = 0.05,
  cont = FALSE,
  lfc = 0,
  f.asso = NULL,
  return.diff = FALSE
)

## S4 method for signature 'omics_array,numeric'
genePeakSelection(
  x,
  peak,
  y = NULL,
  data_log = TRUE,
  durPeak = c(1, 1),
  abs_val = TRUE,
  alpha_diff = 0.05
)

Arguments

Value

A omics_array object.

Author(s)

Frédéric Bertrand , Myriam Maumy-Bertrand.

Examples

 if(require(CascadeData)){
	data(micro_US)
	micro_US<-as.omics_array(micro_US,time=c(60,90,210,390),subject=6)
	data(micro_S)
	micro_S<-as.omics_array(micro_S,time=c(60,90,210,390),subject=6)

  #Basically, to find the 50 more significant expressed genes you will use:
  Selection_1<-geneSelection(x=micro_S,y=micro_US,
  tot.number=50,data_log=TRUE)
  summary(Selection_1)
  
  #If we want to select genes that are differentially 
  #at time t60 or t90 :
  Selection_2<-geneSelection(x=micro_S,y=micro_US,tot.number=30,
  wanted.patterns=
  rbind(c(0,1,0,0),c(1,0,0,0),c(1,1,0,0)))
  summary(Selection_2)

  #To select genes that have a differential maximum of expression at a specific time point.
  
  Selection_3<-genePeakSelection(x=micro_S,y=micro_US,peak=1,
  abs_val=FALSE,alpha_diff=0.01)
  summary(Selection_3)
  }

if(require(CascadeData)){
data(micro_US)
micro_US<-as.omics_array(micro_US,time=c(60,90,210,390),subject=6)
data(micro_S)
micro_S<-as.omics_array(micro_S,time=c(60,90,210,390),subject=6)
#Genes with differential expression at t1
Selection1<-geneSelection(x=micro_S,y=micro_US,20,wanted.patterns= rbind(c(1,0,0,0)))
#Genes with differential expression at t2
Selection2<-geneSelection(x=micro_S,y=micro_US,20,wanted.patterns= rbind(c(0,1,0,0)))
#Genes with differential expression at t3
Selection3<-geneSelection(x=micro_S,y=micro_US,20,wanted.patterns= rbind(c(0,0,1,0)))
#Genes with differential expression at t4
Selection4<-geneSelection(x=micro_S,y=micro_US,20,wanted.patterns= rbind(c(0,0,0,1)))
#Genes with global differential expression 
Selection5<-geneSelection(x=micro_S,y=micro_US,20)

#We then merge these selections:
Selection<-unionOmics(list(Selection1,Selection2,Selection3,Selection4,Selection5))
print(Selection)

#Prints the correlation graphics Figure 4:
summary(Selection,3)

##Uncomment this code to retrieve geneids.
#library(org.Hs.eg.db)
#
#ff<-function(x){substr(x, 1, nchar(x)-3)}
#ff<-Vectorize(ff)
#
##Here is the function to transform the probeset names to gene ID.
#
#library("hgu133plus2.db")
#
#probe_to_id<-function(n){  
#x <- hgu133plus2SYMBOL
#mp<-mappedkeys(x)
#xx <- unlist(as.list(x[mp]))
#genes_all = xx[(n)]
#genes_all[is.na(genes_all)]<-"unknown"
#return(genes_all)
#}
#Selection@name<-probe_to_id(Selection@name)
  }
	

Simulates omicsarray data based on a given network.

Description

Simulates omicsarray data based on a given network.

Usage

## S4 method for signature 'omics_network'
gene_expr_simulation(
  omics_network,
  time_label = 1:4,
  subject = 5,
  peak_level = 100,
  act_time_group = 1:4
)

Arguments

Value

A omics_array object.

Author(s)

Bertrand Frederic, Myriam Maumy-Bertrand.

Examples

data(Net)
set.seed(1)

#We simulate gene expressions according to the network Net
Msim<-Patterns::gene_expr_simulation(
	omics_network=Net,
	time_label=rep(1:4,each=25),
	subject=5,
	peak_level=200)
head(Msim)

Overview of a omics_array object

Description

Overview of a omics_array object.

Usage

## S4 method for signature 'omics_array'
head(x, ...)

Arguments

Methods

list("signature(x = \"ANY\")")

Gives an overview.

list("signature(x = \"omics_array\")")

Gives an overview.

Author(s)

Bertrand Frederic, Myriam Maumy-Bertrand.

Examples

 if(require(CascadeData)){
	data(micro_US)
	micro_US<-as.omics_array(micro_US,time=c(60,90,210,390),subject=6)
	head(micro_US)
	}

Reverse-engineer the network

Description

Reverse-engineer the network.

Usage

## S4 method for signature 'omics_array'
inference(
  M,
  tour.max = 30,
  g = function(x) {
     1/x
 },
  conv = 0.001,
  cv.subjects = TRUE,
  nb.folds = NULL,
  eps = 10^-5,
  type.inf = "iterative",
  Fshape = NULL,
  Finit = NULL,
  Omega = NULL,
  fitfun = "LASSO",
  use.Gram = TRUE,
  error.stabsel = 0.05,
  pi_thr.stabsel = 0.6,
  priors = NULL,
  mc.cores = getOption("mc.cores", 2L),
  intercept.stabpath = TRUE,
  steps.seq = 0.95,
  limselect = 0.95,
  use.parallel = TRUE,
  verbose = TRUE,
  show.error.messages = FALSE
)

Arguments

Details

The fitting built-in fitting functions ('fitfun') provided with the 'Patterns' package are :

LASSO

from the 'lars' package (default value)

LASSO2

from the 'glmnet' package

SPLS

from the 'spls' package

ELASTICNET

from the 'elasticnet' package

stability.c060

from the 'c060' package implementation of stability selection

stability.c060.weighted

a new weighted version of the 'c060' package implementation of stability selection

robust

lasso from the 'lars' package with light random Gaussian noise added to the explanatory variables

selectboost.weighted

a new weighted version of the 'selectboost' package implementation of the selectboost algorithm to look for the more stable links against resampling that takes into account the correlated structure of the predictors. If no weights are provided, equal weigths are for all the variables (=non weighted case).

The weights are viewed as a penalty factors in the penalized regression model: it is a number that multiplies the lambda value in the minimization problem to allow differential shrinkage, [Friedman et al. 2010](https://web.stanford.edu/~hastie/Papers/glmnet.pdf), equation 1 page 3. If equal to 0, it implies no shrinkage, and that variable is always included in the model. Default is 1 for all variables. Infinity means that the variable is excluded from the model. Note that the weights are rescaled to sum to the number of variables.

Value

A omics_network object.

Author(s)

Bertrand Frederic, Myriam Maumy-Bertrand.

Examples

#With simulated data, default shaped F matrix and default LASSO from the lars package
#as fitting function
data(M)
infM <- inference(M)
str(infM)
plot(infM, choice="F", nround=0)
plot(infM, choice="F", nround=1)

#With simulated data, cascade network shaped F matrix (1 group per time measurement case) 
#and default LASSO from the lars package as fitting function
infMcasc <- inference(M, Finit=CascadeFinit(4,4), Fshape=CascadeFshape(4,4))
str(infMcasc)
plot(infMcasc, choice="F", nround=0)
plot(infMcasc, choice="F", nround=1)

#With selection of genes from GSE39411
data(Selection)
infSel <- inference(Selection, Finit=CascadeFinit(4,4), Fshape=CascadeFshape(4,4))
str(infSel)
str(infSel)
plot(infSel, choice="F", nround=0)
plot(infSel, choice="F", nround=1)

Details on some probesets of the affy_hg_u133_plus_2 platform.

Description

Dataset with information on the affy_hg_u133_plus_2 platform such as probeset name (affy_hg_u133_plus_2), ensembl_gene_id, entrezgene, hgnc_symbol, chromosome_name, start_position, end_position and band.

Format

The format is: chr "infos"

Details

Data.frame with 8859 rows and 8 variables.

Author(s)

Bertrand Frederic, Myriam Maumy-Bertrand.

Examples

data(infos)

A example of an inferred network (4 groups case).

Description

This dataset is a network example with 102 nodes, 4 times and 4 groups.

Format

The format is: chr "network"

Details

A network class object [package "Patterns"] with 6 slots.

Author(s)

Bertrand Frederic, Myriam Maumy-Bertrand.

Examples

data(network)
str(network)
plot(network)

A example of an inferred cascade network (2 groups case).

Description

This dataset is a cascade network example with 53 nodes, 4 times and 2 groups.

Format

The format is: chr "network2gp"

Details

A network class object [package "Patterns"] with 6 slots.

Examples

data(network2gp)
str(network2gp)
plot(network2gp)

A example of an inferred cascade network (4 groups case).

Description

This dataset is a cascade network example with 102 nodes, 4 times and 4 groups.

Format

The format is: chr "networkCascade"

Details

A network class object [package "Patterns"] with 6 slots.

Author(s)

Bertrand Frederic, Myriam Maumy-Bertrand.

Examples

data(networkCascade)
str(networkCascade)
plot(networkCascade)

Generates a network.

Description

Generates a network.

Usage

network_random(
  nb,
  time_label,
  exp,
  init,
  regul,
  min_expr,
  max_expr,
  casc.level
)

Arguments

Value

A omics_network object.

Author(s)

Bertrand Frederic, Myriam Maumy-Bertrand.

Examples

set.seed(1)
Net<-network_random(
	nb=100,
	time_label=rep(1:4,each=25),
	exp=1,
	init=1,
	regul=round(rexp(100,1))+1,
	min_expr=0.1,
	max_expr=2,
	casc.level=0.4
	)
plot(Net)

Class "omics_array"

Description

The "omics_array" class

Objects from the Class

Objects can be created by calls of the form new("omics_array", ...).

Author(s)

Bertrand Frederic, Myriam Maumy-Bertrand.

Examples

showClass("omics_array")

Class "omics_network"

Description

The "omics_network" class

Objects from the Class

Objects can be created by calls of the form new("omics_network", ...).

Author(s)

Bertrand Frederic, Myriam Maumy-Bertrand.

Examples

showClass("omics_network")

Class "omics_predict"

Description

The "omics_predict" class

Objects from the Class

Objects can be created by calls of the form new("omics_predict", ...).

Author(s)

Bertrand Frederic, Myriam Maumy-Bertrand.

Examples

showClass("omics_predict")

Plot

Description

Considering the class of the argument which is passed to plot, the graphical output differs.

Usage

## S4 method for signature 'omics_array,ANY'
plot(x, y, ...)

## S4 method for signature 'omics_network,ANY'
plot(
  x,
  y,
  choice = "omics_network",
  nv = 0,
  gr = NULL,
  ini = NULL,
  color.vertex = NULL,
  color.edge = NULL,
  video = TRUE,
  weight.node = NULL,
  ani = FALSE,
  size = c(2000, 1000),
  label_v = 1:dim(x@omics_network)[1],
  horiz = TRUE,
  legend.position = "topleft",
  frame.color = "black",
  label.hub = FALSE,
  nround = 2,
  ani.img.name = "Rplot",
  ani.imgdir = "images",
  ani.htmlfile = "index.html",
  outdir,
  ani.group.legend = "Cluster",
  layout = ini,
  alpha = 1,
  pixmap.color = terrain.colors(20),
  ...
)

## S4 method for signature 'omics_predict,ANY'
plot(
  x,
  time = NULL,
  label_v = NULL,
  frame.color = "white",
  ini = NULL,
  label.hub = FALSE,
  edge.arrow.size = 0.7,
  edge.thickness = 1
)

Arguments

Methods

list("signature(x = \"omics_array\", y = \"ANY\",...)")

x

a omics_array object

list_nv

a vector of cutoff at which the network should be shown

list("signature(x = \"omics_network\", y = \"ANY\",...)")

x

a omics_network object

list()

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). The animation can only be created in the working directory. See the help page of the animation method.

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.

list("signature(x = \"omics_predict\", y = \"ANY\",...)")

x

a omics_predict object

list()

Optional arguments: see plot for omics_network

Author(s)

Bertrand Frederic, Myriam Maumy-Bertrand.

Examples

if(require(CascadeData)){
data(micro_US, package="CascadeData")
micro_US<-as.omics_array(micro_US[1:100,],time=c(60,90,210,390),subject=6)
plot(micro_US)
}

Plot functions for the F matrices.

Description

The graphical output will differ according to the option used.

Usage

plotF(x, choice = "Fshape", nround = 2, pixmap.color = terrain.colors(20))

Arguments

Value

Nothing.

Author(s)

Bertrand Frederic, Myriam Maumy-Bertrand.

Examples

#For numerical/inferred F matrices
plotF(CascadeFinit(4,4),choice="F", nround=1)

if (requireNamespace("pixmap", quietly = TRUE)) {
plotF(CascadeFinit(4,4),choice="Fpixmap")
} else {
plotF(CascadeFinit(4,4),choice="F", nround=1)
}

#For theoritical F matrices
plotF(CascadeFshape(4,4),choice="Fshape")
if (requireNamespace("pixmap", quietly = TRUE)) {
plotF(CascadeFshape(4,4),choice="Fshapepixmap")
} else {
plotF(CascadeFshape(4,4),choice="Fshape")
}

Returns the position of edges in the network

Description

Returns the position of edges in the network Retrieve network position for consistent plotting. Utility function to plot networks.

Usage

## S4 method for signature 'omics_network'
position(net, nv = 0)

Arguments

Value

Matrix with as many rows as the number of edges of the network and three columns (name, xcoord, ycoord).

Methods

list("signature(net = \"omics_network\")")

Returns a matrix with the position of the node. This matrix can then be used as an argument in the plot function.

Author(s)

Bertrand Frederic, Myriam Maumy-Bertrand.

Examples

data(network)
position(network)

Methods for Function predict

Description

Prediction of the gene expressions after a knock-out experience for cascade networks.

Usage

## S4 method for signature 'omics_array'
predict(
  object,
  Omega,
  act_time_group = NULL,
  nv = 0,
  targets = NULL,
  adapt = TRUE
)

Arguments

Details

The plot of prediction of knock down experiments (i.e. targets<>NULL) is still in beta testing for the moment.

Author(s)

Bertrand Frederic, Myriam Maumy-Bertrand.

Examples

data(Selection)
data(infos)
pbst_NR4A1 = infos[infos$hgnc_symbol=="NR4A1", "affy_hg_u133_plus_2"]
pbst_EGR1 = infos[infos$hgnc_symbol=="EGR1", "affy_hg_u133_plus_2"]
gene_IDs = infos[match(Selection@name, infos$affy_hg_u133_plus_), "hgnc_symbol"]

data(networkCascade)
#A nv value can chosen using the cutoff function
nv = .02
NR4A1<-which(is.element(Selection@name,pbst_NR4A1))
EGR1<-which(is.element(Selection@name,pbst_EGR1))
P<-position(networkCascade,nv=nv)

#We predict gene expression modulations within the network if NR4A1 is experimentaly knocked-out. 
prediction_ko5_NR4A1<-predict(Selection,networkCascade,nv=nv,targets=NR4A1,act_time_group=1:4)

#Then we plot the results. Here for example we see changes at time points t2, t3 ans t4:
plot(prediction_ko5_NR4A1,time=2:4,ini=P,label_v=gene_IDs)

#We predict gene expression modulations within the network if EGR1 is experimentaly knocked-out. 
prediction_ko5_EGR1<-predict(Selection,networkCascade,nv=nv,targets=EGR1,act_time_group=1:4)

#Then we plot the results. Here for example we see changes at time point t2, t3 ans t4:
plot(prediction_ko5_EGR1,time=2:4,ini=P,label_v=gene_IDs)

Function to merge probesets

Description

Used to collapse probesets using the collapseRows function of the WGCNA package

Usage

## S4 method for signature 'omics_array'
probeMerge(x, ...)

Arguments

Value

Formal class 'omics_array' [package "Patterns"] with 7 slots

Author(s)

Bertrand Frederic, Myriam Maumy-Bertrand.

Examples

if(require(CascadeData)){
data(micro_S)
D<-as.omics_array(micro_S[1:2000,],1:4,6)
D@gene_ID<-jetset::scores.hgu133plus2[D@name,"EntrezID"]
PM <- probeMerge(D)
}

Replace matrix values by band.

Description

F matrices utility function.

Usage

replaceBand(a, b, k)

Arguments

Value

A matrix (same size as a)

Author(s)

Bertrand Frederic, Myriam Maumy-Bertrand.

Examples

a=matrix(1:9,3,3)
b=matrix(0,3,3)
replaceBand(a,b,0)
replaceBand(a,b,1)
replaceBand(a,b,2)

Replace matrix values triangular lower part and by band for the upper part.

Description

F matrices utility function.

Usage

replaceDown(a, b, k)

Arguments

Value

A matrix (same size as a)

Author(s)

Bertrand Frederic, Myriam Maumy-Bertrand.

Examples

a=matrix(1:9,3,3)
b=matrix(1,3,3)
replaceDown(a,b,0)
replaceDown(a,b,1)
replaceDown(a,b,2)

Replace matrix values triangular upper part and by band for the lower part.

Description

F matrices utility function.

Usage

replaceUp(a, b, k)

Arguments

Value

A matrix (same size as a)

Author(s)

Bertrand Frederic, Myriam Maumy-Bertrand.

Examples

a=matrix(1:9,3,3)
b=matrix(1,3,3)
replaceUp(a,b,0)
replaceUp(a,b,1)
replaceUp(a,b,2)

Show methods

Description

Methods for generic function show

Usage

## S4 method for signature 'omics_array'
show(object)

## S4 method for signature 'omics_network'
show(object)

Arguments

Methods

list("signature(object = \"ANY\")")

list("signature(object = \"omics_array\")")

Print an object of class omics_array

list("signature(object = \"omics_network\")")

Print an object of class omics_network

Author(s)

Bertrand Frederic, Myriam Maumy-Bertrand.

Summary methods

Description

Methods for function summary

Usage

## S4 method for signature 'omics_array'
summary(object, nb.graph = NULL, ...)

Arguments

Methods

list("signature(object = \"ANY\")")

list("signature(object = \"omics_array\")")

method here~~

Author(s)

Bertrand Frederic, Myriam Maumy-Bertrand.

Makes the union between two omics_array objects.

Description

Makes the union between two omics_array objects.

Usage

## S4 method for signature 'omics_array,omics_array'
unionOmics(M1, M2)

Arguments

Methods

list("signature(M1 = \"omics_array\", M2 = \"omics_array\")")

Returns a omics_array object which is the union of M1 and M2.

list("signature(M1 = \"list\", M2 = \"ANY\")")

Returns a omics_array object which is the union of the elements of M1.

Author(s)

Bertrand Frederic, Myriam Maumy-Bertrand.

Examples

if(require(CascadeData)){
data(micro_S, package="CascadeData")
#Create another omicsarray object with 100 genes
Mbis<-M<-as.omics_array(micro_S[1:100,],1:4,6)
#Rename the 100 genes
Mbis@name<-paste(M@name,"bis")
rownames(Mbis@omicsarray) <- Mbis@name
#Union (merge without duplicated names) of the two omicsarrays. 
str(unionOmics(M,Mbis))
}

Cluster a omics_array object: performs the clustering.

Description

Based on soft clustering performed by the Mfuzz package.

Usage

## S4 method for signature 'omics_array,numeric,numeric'
unsupervised_clustering(
  M1,
  clust,
  mestim,
  M2 = NULL,
  data_log = TRUE,
  screen = NULL,
  heatmap = TRUE,
  new.window = TRUE
)

Arguments

Value

An object of class omics_array with the group slot updated by groups deduced from the soft clustering result.

Author(s)

Bertrand Frederic, Myriam Maumy-Bertrand.

Examples

if(require(CascadeData)){
data(micro_S, package="CascadeData")
M<-as.omics_array(micro_S[51:100,],1:4,6)
mc<-unsupervised_clustering_auto_m_c(M)
MwithGrp=unsupervised_clustering(M, 4, mc$m, screen=NULL, heatmap=FALSE, new.window = FALSE)
# Other options
unsupervised_clustering(M, 4, mc$m, screen=c(2,2), heatmap=TRUE, new.window = FALSE)
# Plot the clusters
plot(MwithGrp)
}

Cluster a omics_array object: determine optimal fuzzification parameter and number of clusters.

Description

Based on soft clustering performed by the Mfuzz package.

Usage

## S4 method for signature 'omics_array'
unsupervised_clustering_auto_m_c(
  M1,
  clust = NULL,
  mestim = NULL,
  M2 = NULL,
  data_log = TRUE,
  screen = NULL,
  crange = NULL,
  repeats = NULL,
  cselect = TRUE,
  dminimum = FALSE
)

Arguments

Value

Author(s)

Bertrand Frederic, Myriam Maumy-Bertrand.

Examples

if(require(CascadeData)){
data(micro_S, package="CascadeData")
M<-as.omics_array(micro_S[1:100,],1:4,6)
mc<-unsupervised_clustering_auto_m_c(M)
}