geneSelection: Methods for selecting genes

In Cascade: Selection, Reverse-Engineering and Prediction in Cascade Networks

Methods for selecting genes

Description

Selection of differentially expressed genes.

Usage

## S4 method for signature 'micro_array,micro_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
)

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

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<tot.number<1, tot.number represents the proportion of diffenrentially genes that are selected.
data_log	logical (default to TRUE); should data be logged ?
wanted.patterns	a matrix with wanted patterns [only for geneSelection].
forbidden.patterns	a matrix with forbidden patterns [only for geneSelection].
peak	interger. At which time points measurements should the genes be selected [optionnal for geneSelection].
alpha	float; the risk level. Default to 'alpha=0.05'
Design	the design matrix of the experiment. Defaults to 'NULL'.
lfc	log fold change value used in limma's 'topTable'. Defaults to 0.
cont	use contrasts. Defaults to 'FALSE'.
f.asso	function used to assess the association between the genes. The default value 'NULL' implies the use of the usual 'mean' function.
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]
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, Frédéric Bertrand , Myriam Maumy-Bertrand.

References

Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2014). 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

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

Cascade documentation built on Nov. 28, 2022, 5:10 p.m.