/
omics_array.R
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omics_array.R
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#' \code{Show} methods
#'
#' Methods for generic function \code{show}
#'
#'
#' @name show-methods
#' @aliases show-methods show,ANY-method show,omics_array-method
#' show,omics_network-method
#' @docType methods
#' @section Methods: \describe{
#' \item{list("signature(object = \"ANY\")")}{ }
#' \item{list("signature(object = \"omics_array\")")}{ Print an object of class omics_array }
#' \item{list("signature(object = \"omics_network\")")}{ Print an object of class omics_network }
#' }
#'
#' @param object an object of class omics-array or omics_network
#'
#' @author Bertrand Frederic, Myriam Maumy-Bertrand.
#' @keywords methods
#'
setMethod("show","omics_array",function(object)
{
cat(paste("This is a omics_array S4 class. It contains : \n - (@omicsarray) a matrix of dimension ",
dim(object@omicsarray)[1],"*",dim(object@omicsarray)[2],
"\n .... [omics expressions (or abundancies)] \n - (@name) a vector of length ",
length(object@name),
" .... [omics names (i.e. gene or peptides names)] \n","\n - (@gene_ID) a vector of length ",
length(object@gene_ID),
" .... [omics ID] \n","- (@group) a vector of length ",
length(object@group),
" .... [groups for omics] \n","- (@start_time) a vector of length ",
length(object@start_time),
"\n .... [first differential expression for omics] \n",
"- (@time)a vector of length ",
length(object@time),
" .... [time points]\n",
"- (@subject) an integer .... [number of subject]"))
invisible(object)
})
#' Overview of a omics_array object
#'
#' Overview of a omics_array object.
#'
#'
#' @aliases methods head-methods head,ANY-method head,omics_array-method
#' @section Methods: \describe{
#'
#' \item{list("signature(x = \"ANY\")")}{ Gives an overview. }
#'
#' \item{list("signature(x = \"omics_array\")")}{ Gives an overview. } }
#' @param x an object of class `omics_array`.
#' @param ... additional parameters
#' @author Bertrand Frederic, Myriam Maumy-Bertrand.
#' @keywords methods
#' @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)
#' }
#'
setMethod("head",signature="omics_array",function(x,...)
{
K<-dim(x@omicsarray)[2]
K<-min(K,3)
return(list(omicsarray=head(x@omicsarray[,1:K]),
name=head(x@name),
gene_ID=head(x@gene_ID),
group=head(x@group),
start_time=head(x@start_time),
time=x@time,
subject=x@subject))
}
)
#' \code{Summary} methods
#'
#' Methods for function \code{summary}
#'
#'
#' @name summary-methods
#' @aliases summary-methods summary,ANY-method summary,omics_array-method
#' @docType methods
#' @section Methods: \describe{
#'
#' \item{list("signature(object = \"ANY\")")}{ %% ~~describe this method here~~
#' }
#'
#' \item{list("signature(object = \"omics_array\")")}{ %% ~~describe this
#' method here~~ } }
#'
#' @param object an object of class omics-array
#' @param nb.graph (optionnal) choose the graph to plot. Displays all graphs by default.
#' @param ... additional parameters.
#'
#' @author Bertrand Frederic, Myriam Maumy-Bertrand.
#' @keywords methods
#'
setMethod("summary","omics_array",function(object,nb.graph=NULL,...)
{
require(cluster)
print(summary(object@omicsarray))
G<-object@omicsarray
colnames(G)<-paste(rep(paste("T",object@time),object@subject), as.character(rep(paste("subject",1:object@subject),each=length(object@time))))
z <- cor(G)
require(lattice)
#rownames(z)<-NULL
if(is.null(nb.graph) || nb.graph==1 ){
print(lattice::levelplot(z,aspect="iso",xlab="",ylab="",
scales=list(x=list(rot=90)),
ylab.right = "Level of correlation",
par.settings = list(layout.widths = list(axis.key.padding = 0,
ylab.right = 2))))
}
#if(is.null(nb.graph)){dev.new()}
G<-object@omicsarray
colnames(G)<-paste(rep(paste("T",object@time),object@subject), as.character(rep(paste("subject",1:object@subject),each=length(object@time))))
w<-cluster::agnes(t(G))[1]$order
G<-G[,w]
z <- cor(G)
if(is.null(nb.graph) || nb.graph==2 ){
print(lattice::levelplot(z,aspect="iso",xlab="",ylab="",
scales=list(x=list(rot=90)),
ylab.right = "Level of correlation",
par.settings = list(layout.widths = list(axis.key.padding = 0,
ylab.right = 2))))
}
if(dim(object@omicsarray)[1]<1000){
#if(is.null(nb.graph)){dev.new()}
R<-object@omicsarray
w<-cluster::agnes(R)[1]$order
R<-R[w,]
z <- cor(t(R))
if(is.null(nb.graph) || nb.graph==3 ){
print(lattice::levelplot(z,aspect="iso",xlab="",ylab="",
scales=list(x=list(rot=90)),
ylab.right = "Level of correlation",
par.settings = list(layout.widths = list(axis.key.padding = 0,
ylab.right = 2))))
}
}}
)
#' Dimension of the data
#'
#' Dimension of the data
#'
#'
#' @name dim
#' @aliases dim dim-methods dim,omics_array-method
#' @docType methods
#' @section Methods: \describe{
#'
#' \item{list("signature(x = \"omics_array\")")}{ Gives the dimension of the
#' matrix of measurements. } }
#' @param x an object of class `omics_array`.
#' @author Bertrand Frederic, Myriam Maumy-Bertrand.
#' @keywords methods
setMethod("dim","omics_array",function(x)
{
return(dim(x@omicsarray))
})
#' Plot
#'
#' Considering the class of the argument which is passed to plot, the graphical
#' output differs.
#'
#'
#' @name plot-methods
#' @aliases plot-methods plot,omics_array,ANY-method
#' plot,omics_predict,ANY-method plot,omics_network,ANY-method
#' @docType methods
#' @section Methods: \describe{
#'
#' \item{list("signature(x = \"omics_array\", y = \"ANY\",...)")}{ \describe{
#' \item{x}{a omics_array object} \item{list_nv}{a vector of cutoff at which
#' the network should be shown} } } \item{list("signature(x = \"omics_network\", y =
#' \"ANY\",...)")}{ \describe{ \item{x}{a omics_network object}
#' \item{list()}{Optionnal arguments: \describe{ \item{gr}{a vector giving the
#' group of each gene} \item{choice}{what graphic should be plotted: either "F"
#' (for a representation of the matrices F) or "network".} \item{nv}{the level
#' of cutoff. Defaut to 0.} \item{ini}{using the ``position'' function, you can
#' fix the position of the nodes} \item{color.vertex}{a vector defining the
#' color of the vertex} \item{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.} \item{video}{if ani is TRUE and
#' video is TRUE, the animation result is a GIF video} \item{label_v}{vector
#' defining the vertex labels} \item{legend.position}{position of the legend}
#' \item{frame.color}{color of the frames} \item{label.hub}{logical ; if TRUE
#' only the hubs are labeled} \item{edge.arrow.size}{size of the arrows ;
#' default to 0.7} \item{edge.thickness}{edge thickness ; default to 1.} } }}}
#'
#' \item{list("signature(x = \"omics_predict\", y = \"ANY\",...)")}{ \describe{
#' \item{x}{a omics_predict object} \item{list()}{Optional arguments: see plot
#' for omics_network} }} }
#'
#' @param x a omics_array object, a omics_network object or a omics_predict object
#' @param y optional and not used if x is an appropriate structure
#' @param gr a vector giving the group of each gene
#' @param choice what graphic should be plotted: either "F"
#' (for a representation of the matrices F) or "network".
#' @param nv the level of cutoff. Defaut to `0`.
#' @param ini using the ``position'' function, you can
#' fix the position of the nodes.
#' @param color.vertex a vector defining the color of the vertex.
#' @param ani animated plot?
#' @param size vector giving the size of the plot. Default to `c(2000,1000)`.
#' @param video if ani is TRUE and video is TRUE, the result of the animation is saved as an animated GIF.
#' @param label_v vector defining the vertex labels.
#' @param legend.position position of the legend.
#' @param frame.color color of the frames.
#' @param label.hub logical ; if TRUE only the hubs are labeled.
#' @param edge.arrow.size size of the arrows ; default to 0.7.
#' @param edge.thickness edge thickness ; default to 1.
#' @param weight.node nodes weighting. Defaults to `NULL`.
#' @param horiz landscape? Defaults to `TRUE`.
#' @param time sets the time for plot of the prediction. Defaults to `NULL`
#' @param color.edge color of the edges
#' @param nround number of digits to display
#' @param ani.img.name name of image file for animations
#' @param ani.imgdir name of the image directory for animations
#' @param ani.htmlfile name of the html file for animations
#' @param outdir name of the outdir for animations
#' @param ani.group.legend legend for animations
#' @param layout layout of the graphs
#' @param alpha transparency of the graphs
#' @param pixmap.color color for pixmap graphs
#' @param ... additional parameters
#'
#' @author Bertrand Frederic, Myriam Maumy-Bertrand.
#' @keywords methods
#' @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)
#' }
#'
setMethod("plot","omics_array",function(x,...)
{
requireNamespace("lattice")
#requireNamespace("grid")
xs<-t(x@omicsarray)
rownames(xs)<-1:dim(xs)[1]
ys<-x@time
YS<-rep(ys,x@subject)
suj<- rep(paste("Subject",1:x@subject,sep=" "),each=length(ys))
ID<-paste("ID",1:dim(xs)[1],sep="")
cclus<-unique(x@group)
cclus<-cclus[order(cclus)]
U<-data.frame(xs,suj,ys)
V<- reshape(U,idvar="ID",varying=list(1:dim(xs)[2]), v.names = "conc", direction = "long")
if(length(unique(x@group))>1){
gr<-rep(paste("Cluster",x@group,sep=" "),each=x@subject*length(x@time))
#if(!attr(dev.cur(),"names")=="pdf"){dev.new()}
print(lattice::xyplot(V$conc~V$ys|V$suj,as.table=TRUE,xlab="Time",ylab="Gene Expression",group=rep(1:(x@subject*dim(xs)[2]),each=length(x@time)),type="l",scales=list(x=list(relation="free",at=x@time),y=list(relation="free")),col=rep(x@group,each=x@subject),key=list(
space="right",
lines=list(type="l",col=cclus),
text=list(text=paste("Cluster",as.character(cclus)))
)))
#if(!attr(dev.cur(),"names")=="pdf"){dev.new()}
print(lattice::xyplot(V$conc~V$ys|gr,as.table=TRUE,xlab="Time",ylab="Gene Expression",group=rep(1:(x@subject*dim(xs)[2]),each=length(x@time)),type="l",scales=list(x=list(relation="free",at=x@time),y=list(relation="free")),col=rep(1:x@subject,dim(xs)[2]),key=list(
space="right",
lines=list(type="l",col=1:x@subject),
text=list(text=paste("Subject",as.character(1:x@subject)))
)))
for(i in 1:x@subject){
ss<-V$suj
sss<-ss==paste("Subject",i,sep=" ")
#if(!attr(dev.cur(),"names")=="pdf"){dev.new()}
print(lattice::xyplot(V$conc[sss]~V$ys[sss]|gr[sss],as.table=TRUE,xlab="Time",ylab="Gene Expression",type="l",main=paste("Subject",i,sep=" "),group=rep(1:(x@subject*dim(xs)[2]),each=length(x@time))[sss],scales=list(x=list(relation="free",at=x@time),y=list(relation="free"))))
}
}
else{
lattice::xyplot(V$conc~V$ys|V$suj,xlab="Time",as.table=TRUE,group=rep(1:(x@subject*dim(xs)[2]),each=length(x@time)),ylab="Gene Expression",scales=list(x=list(relation="free",at=x@time),y=list(relation="free")),type="l",col="black")
}
}
)
#' Function to merge probesets
#'
#' Used to collapse probesets using the collapseRows function of the WGCNA
#' package
#'
#'
#' @aliases probeMerge probeMerge,omics_array-method
#' @param x omicsarray
#' @param \dots Additionnal parameters to the collapseRows function of the
#' WGCNA package
#' @return Formal class 'omics_array' [package "Patterns"] with 7 slots
#' @author Bertrand Frederic, Myriam Maumy-Bertrand.
#' @keywords manip
#' @examples
#'
#' if(require(CascadeData)){
#' data(micro_S)
#' D<-as.omics_array(micro_S[1:2000,],1:4,6)
#' D@gene_ID<-jetset::scores.hgu133plus2[D@name,"EntrezID"]
#' PM <- probeMerge(D)
#' }
#'
setMethod("probeMerge","omics_array",function(x,...)
{
#requireNamespace('WGCNA',quietly = TRUE)
probeID<-x@name
geneID<-x@gene_ID
M<-x@omicsarray
NAsPositions<-which(is.na(geneID))
geneID[NAsPositions]<-paste("Unknown",1:length(NAsPositions))
selec<-WGCNA::collapseRows(datET=M,rowGroup=geneID,rowID=probeID,...)
M1<-new("omics_array"
,omicsarray=selec$datETcollapsed
,name=selec$group2row[,2]
,gene_ID=selec$group2row[,1]
,time=x@time
,subject=x@subject
,group=x@group[selec$selectedRow]
,start_time=x@start_time[selec$selectedRow])
return(M1)
})
#' Cluster a omics_array object: determine optimal fuzzification parameter and
#' number of clusters.
#'
#' Based on soft clustering performed by the Mfuzz package.
#'
#'
#' @aliases unsupervised_clustering_auto_m_c
#' unsupervised_clustering_auto_m_c,omics_array-method
#' @param M1 Object of omics_array class.
#' @param clust [NULL] Number of clusters.
#' @param mestim [NULL] Fuzzification parameter.
#' @param M2 [NULL] Object of omics_array class,
#' @param data_log [TRUE] Should data be logged?
#' @param screen [NULL] Specify `screen` parameter.
#' @param crange [NULL] Specify `crange` parameter.
#' @param repeats [NULL] Specify `repeats` parameter.
#' @param cselect [TRUE] Estimate `cselect` parameter.
#' @param dminimum [FALSE] Estimate `dminimum` parameter.
#'
#' @return \item{m}{Estimate of the optimal fuzzification parameter.}
#' \item{c}{Estimate of the optimal number of clusters.} \item{csearch}{More
#' result from the cselection function of the Mfuzz package}
#' @author Bertrand Frederic, Myriam Maumy-Bertrand.
#' @keywords cluster
#' @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)
#' }
#'
setMethod(f="unsupervised_clustering_auto_m_c",
signature=c("omics_array"),
definition=function(M1,
clust=NULL,
mestim=NULL,
M2=NULL,
data_log=TRUE,
screen=NULL,
crange=NULL,
repeats=NULL,
cselect=TRUE,
dminimum=FALSE){
require("Mfuzz", quietly = TRUE, warn.conflicts = FALSE)
if(is.null(crange)){crange=seq(4,32,4)}
if(is.null(repeats)){repeats=5}
if(is.null(M2)){
if(data_log==TRUE){
M<-log(M1@omicsarray)
}
else{
M<-(M1@omicsarray)
}
} else {
if(data_log==TRUE){
M1_mic<-log(M1@omicsarray)
M2_mic<-log(M2@omicsarray)
}
else{
M1_mic<-M1@omicsarray
M2_mic<-M2@omicsarray
}
M=M1_mic-M2_mic
}
colnames(M)<-NULL
Em<-Biobase::ExpressionSet(M)
Em.s <- Mfuzz::standardise(Em)
if(is.null(mestim)){mestim <- Mfuzz::mestimate(Em.s)
}
if(cselect){
if(is.null(clust)){
tmp <- Mfuzz::cselection(Em.s,m=mestim,crange=crange,repeats=repeats,visu=TRUE)
clust <- ifelse(which.max(rowSums(t(tmp)-crange)<0)==1,crange[dim(tmp)[2]],crange[which.max(rowSums(t(tmp)-crange)<0)-1])
} else {tmp=NA}
}
if(dminimum){
if(is.null(clust)){
tmp <- Mfuzz::Dmin(Em.s,m=mestim,crange=crange,repeats=repeats,visu=TRUE)
clust <- NA
} else {tmp=NA}
}
return(list(m=mestim,c=clust,csearch=tmp))
}
)
#' Cluster a omics_array object: performs the clustering.
#'
#' Based on soft clustering performed by the Mfuzz package.
#'
#'
#' @aliases unsupervised_clustering
#' unsupervised_clustering,omics_array,numeric,numeric-method
#' @param M1 Object of omics_array class.
#' @param clust Number of clusters.
#' @param mestim Fuzzification parameter.
#' @param M2 [NULL] Object of omics_array class,
#' @param data_log [TRUE] Should data be logged?
#' @param screen [NULL] Specify `mfrow` parameter.
#' @param heatmap [TRUE] Plot heatmaps?
#' @param new.window [TRUE] Use new window?
#'
#' @return An object of class omics_array with the group slot updated by groups
#' deduced from the soft clustering result.
#' @author Bertrand Frederic, Myriam Maumy-Bertrand.
#' @keywords cluster
#' @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)
#' }
#'
setMethod(f="unsupervised_clustering",
signature=c("omics_array","numeric","numeric"),
definition=function(M1,
clust,
mestim,
M2=NULL,
data_log=TRUE,
screen=NULL,
heatmap=TRUE,
new.window=TRUE){
require("Mfuzz", quietly = TRUE, warn.conflicts = FALSE)
if(is.null(M2)){
if(data_log==TRUE){
M<-log(M1@omicsarray)
}
else{
M<-(M1@omicsarray)
}
} else {
if(data_log==TRUE){
M1_mic<-log(M1@omicsarray)
M2_mic<-log(M2@omicsarray)
}
else{
M1_mic<-(M1@omicsarray)
M2_mic<-(M2@omicsarray)
}
M=M1_mic-M2_mic
}
colnames(M)<-NULL
Em<-Biobase::ExpressionSet(M)
Em.s <- Mfuzz::standardise(Em)
cl <- Mfuzz::mfuzz(Em.s,c=clust,m=mestim)
Mfuzz::overlap.plot(cl,over =Mfuzz::overlap(cl), thres = 0.05)
if(!attr(dev.cur(),"names")=="pdf"){
if(is.null(screen)){Mfuzz::mfuzz.plot(Em.s,cl,new.window=new.window)
} else {Mfuzz::mfuzz.plot(Em.s,cl,mfrow=screen,new.window=new.window)}} else {
if(is.null(screen)){Mfuzz::mfuzz.plot(Em.s,cl,new.window=FALSE)
} else {Mfuzz::mfuzz.plot(Em.s,cl,mfrow=screen,new.window=FALSE)}}
if(heatmap){
require(gplots,quietly = TRUE)
#if(!attr(dev.cur(),"names")=="pdf"){dev.new()}
gplots::heatmap.2(Biobase::exprs(Em.s), dendrogram = 'row', Colv = FALSE, col = gplots::greenred(75),
key = FALSE, keysize = 1.0, symkey = FALSE, density.info = 'none',
trace = 'none', colsep = rep(1:10), sepcolor = 'white', sepwidth = 0.05,
hclustfun = function (c){hclust(c, method = 'average')},
labRow = NA, cexCol = 1)
}
MM<-M1
MM@group<-cl$cluster
return(MM)
}
)
#' Methods for selecting genes
#'
#' Selection of differentially expressed genes.
#'
#'
#' @name geneSelection
#' @aliases genePeakSelection geneSelection genePeakSelection-methods
#' geneSelection-methods geneSelection,omics_array,numeric-method
#' geneSelection,omics_array,omics_array,numeric-method
#' geneSelection,list,list,numeric-method
#' genePeakSelection,omics_array,numeric-method
#' @param x either a omics_array object or a list of omics_array objects. In
#' the first case, the omics_array object represents the stimulated
#' measurements. In the second case, the control unstimulated data (if present)
#' should be the first element of the list.
#' @param y either a omics_array object or a list of strings. In the first
#' case, the omics_array object represents the stimulated measurements. In the
#' second case, the list is the way to specify the contrast: \describe{
#' \item{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.} \item{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.} \item{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. }}
#' @param 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<tot.number<1,
#' tot.number represents the proportion of diffenrentially genes that are
#' selected.
#' @param peak interger. At which time points measurements should the genes be
#' selected [optionnal for geneSelection].
#' @param data_log logical (default to TRUE); should data be logged ?
#' @param wanted.patterns a matrix with wanted patterns [only for geneSelection].
#' @param forbidden.patterns a matrix with forbidden patterns [only for geneSelection].
#' @param durPeak vector of size 2 (default to c(1,1)) ; the first elements gives the length of the peak at
#' the left, the second at the right. [only for genePeakSelection]
#' @param abs_val logical (default to TRUE) ; should genes be selected on the
#' basis of their absolute value expression ? [only for genePeakSelection]
#' @param alpha_diff float; the risk level
#' @param alpha float; the risk level. Default to `alpha=0.05`
#' @param Design the design matrix of the experiment. Defaults to `NULL`.
#' @param lfc log fold change value used in limma's `topTable`. Defaults to 0.
#' @param cont use contrasts. Defaults to `FALSE`.
#' @param f.asso function used to assess the association between the genes.
#' The default value `NULL` implies the use of the usual `mean` function.
#' @param return.diff [FALSE] if TRUE then the function returns the stimulated expression of the differentially expressed genes
#' @return A omics_array object.
#' @author Frédéric Bertrand , Myriam Maumy-Bertrand.
#' @keywords methods
#' @examples
#'
#' \donttest{
#' 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)
#' }
#' }
#'
setMethod(
f = "geneSelection",
signature = c("omics_array", "omics_array", "numeric"),
definition = function(x,
y,
tot.number,
data_log = TRUE,
wanted.patterns = NULL,
forbidden.patterns = NULL,
peak = NULL,
alpha = 0.05,
Design = NULL,
lfc = 0) {
warnings(
"The use of the geneSelection with signature c(omics_array,omics_array) is depreciated",
immediate. = TRUE
)
if (!is.null(sessionInfo()$otherPkgs$limma$Version)) {
BBB <- strsplit(sessionInfo()$otherPkgs$limma$Version, "[.]")
} else {
if (!is.null(sessionInfo()$loadedOnly$limma$Version)) {
BBB <- strsplit(sessionInfo()$loadedOnly$limma$Version, "[.]")
} else {
stop("Please install the limma BioConductor package")
}
}
if (!(BBB[[1]][1] > 3 ||
(BBB[[1]][1] == 3 && BBB[[1]][2] > 18) ||
(BBB[[1]][1] == 3 &&
BBB[[1]][2] == 18 && BBB[[1]][3] >= 13)))
{
stop("Upgrade your version of Limma (>= 3.18.13)")
}
indic <- 0
M1 <- x
M2 <- y
if (is.null(M2)) {
indic <- 1
M2 <- M1
}
require(limma)
if (data_log == TRUE) {
M1_mic <- log(M1@omicsarray)
M2_mic <- log(M2@omicsarray)
} else{
M1_mic <- (M1@omicsarray)
M2_mic <- (M2@omicsarray)
}
if (is.null(rownames(M1_mic))) {
rownames(M1_mic) <- paste("probe ", 1:dim(M1_mic)[1])
}
if (is.null(rownames(M2_mic))) {
rownames(M2_mic) <- paste("probe ", 1:dim(M2_mic)[1])
}
if (indic == 1) {
M2_mic <- M2_mic * 0
}
colnames(M1_mic) <-
paste(rep("US", length(M1@time) * M1@subject),
rep(M1@time, M1@subject),
sep = "")
colnames(M2_mic) <-
paste(rep("S", length(M2@time) * M2@subject), rep(M2@time, M2@subject), sep =
"")
#rownames(M1_mic)<- paste("probe",rownames(M1_mic) )
#rownames(M2_mic)<- paste("probe",rownames(M2_mic) )
M <- cbind(M1_mic, M2_mic)
T <- length(M1@time)
#Construction de la matrice de design
if (is.null(Design)) {
design <- t(matrix(rep(0, (
M1@subject + M2@subject
) * T * (T * 2)), 2 * T))
for (i in 1:(T)) {
design[which(colnames(M) %in% paste("US", M1@time[i], sep = "")), i] <-
1
design[which(colnames(M) %in% paste("S", M2@time[i], sep =
"")), i + T] <- 1
}
vnom <-
paste(c(
paste(rep("US_time", length(M1@time)), M1@time[1:(length(M1@time))], sep =
""),
paste(rep("S_time", length(M1@time)), M1@time[1:(length(M1@time))], sep =
"")
), sep = "")
colnames(design) <- vnom
} else{
design <- Design$X
}
#block<-rep(1:(M1@subject*2),each=T)
#dupcor <- duplicateCorrelation(M,design,block=block)
#model<-lmFit(M,design,block=block)
model <- lmFit(M, design)
if (is.null(Design)) {
diff <- paste(vnom[1:T], vnom[1:T + length(M1@time)], sep = "-")
contr <-
makeContrasts(contrasts = diff, levels = vnom)
} else{
contr <- Design$contr
}
model2 <- contrasts.fit(model, contr)
model.test <- eBayes(model2)
p.val.ind <- model.test$p.value
rownames(p.val.ind) <- rownames(M1_mic)
p.val.all <-
topTable(model.test,
p.value = alpha,
number = dim(M)[1],
lfc = lfc)
kkkk <- 0
if (is.null(p.val.all$ID)) {
kkkk <- 1
ID <- row.names(p.val.all)
p.val.all <- cbind(ID, p.val.all)
f_nom <- function(nom) {
which(x@name %in% nom)
}
f_nom <- Vectorize(f_nom)
row.names(p.val.all) <-
unlist(f_nom(as.character(p.val.all$ID)))
}
if (tot.number > 0 && tot.number <= 1) {
tot.number <- round(tot.number * dim(p.val.all)[1])
}
if (kkkk <- 0) {
ind.class <- rownames(p.val.all)
p.val.ind.class <- p.val.ind[(ind.class), ]
} else{
ind.class <- rownames(p.val.all)
p.val.ind.class <- p.val.ind[as.numeric(ind.class), ]
}
f.p.val <- function(x) {
if (x < alpha) {
return(1)
}
else{
return(0)
}
}
p.val.ind.class.cat <-
apply(p.val.ind.class, c(1, 2), f.p.val)
choix <-
cbind(p.val.ind.class.cat, rep(0, dim(p.val.ind.class.cat)[1]))
ch <- dim(choix)[2]
f_test_patt <- function(x, pat) {
if (sum(abs(x - pat)) == 0) {
return(1)
}
else{
return(0)
}
}
if (!is.null(forbidden.patterns)) {
for (i in 1:dim(forbidden.patterns)[1]) {
f_test2 <- function(x) {
f_test_patt(x, forbidden.patterns[i, ])
}
S <- apply(choix[, 1:(ch - 1)], 1, f_test2)
choix <- choix[S == 0, ]
}
}
if (!is.null(wanted.patterns)) {
sel <- rep(0, dim(choix)[1])
choix[, ch] <- choix[, ch] * 0
for (i in 1:dim(wanted.patterns)[1]) {
f_test2 <- function(x) {
f_test_patt(x, wanted.patterns[i, 1:T])
}
sel <- sel + apply(choix[, 1:(ch - 1)], 1, f_test2)
}
for (j in 1:length(sel)) {
if ((sum(choix[1:(j - 1), ch]) < tot.number ||
tot.number < 0) && sel[j] >= 1) {
choix[j, ch] <- 1
}
}
}
f_test_peak <- function(x, peak) {
x[peak]
}
if (!is.null(peak)) {
f_test2 <- function(x) {
f_test_peak(x, peak)
}
# if(tot.number>0){
# S<-apply(choix[,1:(ch-1)],1,f_test2)
# for(j in 1:length(S)){
# if(sum(S[1:j])<=tot.number && S[j]==1){choix[j,ch]<-1}
# }
# }
# else{
choix[, ch] <- apply(choix[, 1:(ch - 1)], 1, f_test2)
#}
}
if (tot.number > 0 && is.null(wanted.patterns)) {
i <- 1
PN <- dim(choix)[1]
PN <- 1:PN
if (is.null(peak)) {
while (sum(choix[, ch]) < tot.number) {
choix[i, ch] <- 1
i <- i + 1
}
choix[PN >= i, ch] <- 0
choix <- choix[choix[, ch] == 1, ]
}
else{
choix <- choix[choix[, ch] == 1, ]
PN <- dim(choix)[1]
choix <- choix[1:min(PN, tot.number), ]
}
}
if ((!is.null(wanted.patterns))) {
choix <- choix[choix[, ch] == 1, ]
}
if (!is.null(peak) &&
tot.number <= 0) {
choix <- choix[choix[, ch] == 1, ]
}
temps.prem <- function(x) {
u <- 0
i <- 1
while (u == 0) {
if (x[i] == 1) {
return(i)
}
else
i = i + 1
}
}
R <- apply(choix, 1, temps.prem)
n <- length(R)
MM1 <- M1_mic[rownames(choix), ] - M2_mic[rownames(choix), ]
r3 <- function(choix) {
sortie <- NULL
for (i in 1:length(choix)) {
sortie <- c(sortie, which(rownames(M1_mic) %in% rownames(choix)[i]))
}
sortie
}
M <-
new(
"omics_array",
omicsarray = MM1,
name = M1@name[r3(choix)],
gene_ID = M1@gene_ID[r3(choix)],
time = M1@time,
subject = M1@subject,
group = as.vector(R),
start_time = as.vector(R)
)
return(M)
}
)
#' @rdname geneSelection
setMethod(
f = "geneSelection",
signature = c("list", "list", "numeric"),
definition = function(x,
y,
tot.number,
data_log = TRUE,
alpha = 0.05,
cont = FALSE,
lfc = 0,
f.asso = NULL,
return.diff=FALSE) {
#here we check if the version of Limma is ok
if (!is.null(sessionInfo()$otherPkgs$limma$Version)) {
BBB <- strsplit(sessionInfo()$otherPkgs$limma$Version, "[.]")
} else {
if (!is.null(sessionInfo()$loadedOnly$limma$Version)) {
BBB <- strsplit(sessionInfo()$loadedOnly$limma$Version, "[.]")
} else {
stop("Please install the limma BioConductor package")
}
}
if (!(BBB[[1]][1] > 3 ||
(BBB[[1]][1] == 3 && BBB[[1]][2] > 18) ||
(BBB[[1]][1] == 3 &&
BBB[[1]][2] == 18 && BBB[[1]][3] >= 13)))
{
stop("Upgrade your version of Limma (>= 3.18.13)")
}
M <- x
contrast <- y
M_mic <- M
require(limma)
n <- length(M)
Time <- length(M[[2]]@time)
if (cont == TRUE && is.null(f.asso)) {
f.asso <- "mean"
}