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Last data update: 2014.03.03

R: Graphical Display of The expression levels

plotGene

R Documentation

Graphical Display of The expression levels

Description

Plot the expression values and print statistical results for each individual gene based on a user query

Usage

     plotGene(gene.to.plot,x,gene.names=NULL, data, cl,origin,
              logged=TRUE,logbase=2)

Arguments

`gene.to.plot`	Name of the gene to be plotted
`x`	the value returned by function RP, RPadvance or RSadvance
`gene.names`	Names of all genes in the data set. If "NULL", rownames of the data will be used
`data`	the same as that used in RP or RPadvance
`cl`	the same as that used in RP or RPadvance
`origin`	a vector containing the origin labels of the sample. e.g. for the data sets generated at multiple laboratories, the label is the same for samples within one lab and different for samples from different labs. The same as that used in RPadvance
`logged`	if "TRUE", data has bee logged, otherwise set it to "FALSE"
`logbase`	base used when taking log, used to restore the fold change.The default value is 2, this will be ignored if logged=FALSE

Value

A graphical display of the expression levels of the input gene. The estimated statistics for differential expression will be printed on the plot as well as output in the screen.The statistics include: F.C.:fold-change under each dataset if multiple datasets are used AveFC: average fold-change across all datasets pfp(pval): estimated percentage of false prediction (p-value) for differential expression under each of the two tests: up-regulation in class 2 compared with classs 1 and down-regulation in class 2 compared with class 1

Author(s)

Fangxin Hong fhong@salk.edu

Examples

     
      # Load the data of Golub et al. (1999). data(golub) 
      #contains a 3051x38 gene expression
      # matrix called golub, a vector of length called golub.cl 
      #that consists of the 38 class labels,
      # and a matrix called golub.gnames whose third column contains the gene names.
      data(golub)
 
      #use a subset of data as example, apply the rank product method
      subset <- c(1:4,28:30)
      #Setting rand=123, to make the results reproducible,

      #identify genes that are up-regulated in class 2 
      #(class label =1)
      RP.out <- RP(golub[,subset],golub.cl[subset], rand=123)
      
      #plot the results
      plotRP(RP.out,cutoff=0.05)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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Type 'license()' or 'licence()' for distribution details.

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Type 'demo()' for some demos, 'help()' for on-line help, or
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> library(RankProd)
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/RankProd/plotGene.Rd_%03d_medium.png", width=480, height=480)
> ### Name: plotGene
> ### Title: Graphical Display of The expression levels
> ### Aliases: plotGene
> ### Keywords: htest
> 
> ### ** Examples
> 
>      
>       # Load the data of Golub et al. (1999). data(golub) 
>       #contains a 3051x38 gene expression
>       # matrix called golub, a vector of length called golub.cl 
>       #that consists of the 38 class labels,
>       # and a matrix called golub.gnames whose third column contains the gene names.
>       data(golub)
>  
>       #use a subset of data as example, apply the rank product method
>       subset <- c(1:4,28:30)
>       #Setting rand=123, to make the results reproducible,
> 
>       #identify genes that are up-regulated in class 2 
>       #(class label =1)
>       RP.out <- RP(golub[,subset],golub.cl[subset], rand=123)
Rank Product analysis for two-class case 
 
Starting 100 permutations... 
Computing pfp .. 
Outputing the results .. 
>       
>       #plot the results
>       plotRP(RP.out,cutoff=0.05)
>       
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>