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Last data update: 2014.03.03

R: Advanced Rank Sum Analysis of Microarray

RSadvance

R Documentation

Advanced Rank Sum Analysis of Microarray

Description

Advance rank sum method to identify differentially expressed genes. It is possible to combine data from different studies, e.g. data sets generated at different laboratories.

Usage

    RSadvance(data,cl,origin,num.perm=100,logged=TRUE,
                 na.rm=FALSE,gene.names=NULL,plot=FALSE, rand=NULL)

Arguments

`data`	the data set that should be analyzed. Every row of this data set must correspond to a gene.
`cl`	a vector containing the class labels of the samples. In the two class unpaired case, the label of a sample is either 0 (e.g., control group) or 1 (e.g., case group). For one group data, the label for each sample should be 1.
`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.
`num.perm`	number of permutations used in the calculation of the null density. Default is 'B=100'.
`logged`	if "TRUE", data has bee logged, otherwise set it to "FALSE"
`na.rm`	if 'FALSE' (default), the NA value will not be used in computing rank. If 'TRUE', the missing values will be replaced by the genewise mean of the non-missing values. Gene will all value missing will be assigned "NA"
`gene.names`	if "NULL", no gene name will be attached to the estimated percentage of false prediction (pfp).
`plot`	If "TRUE", plot the estimated pfp verse the rank of each gene
`rand`	if specified, the random number generator will be put in a reproducible state.

Value

A result of identifying differentially expressed genes between two classes. The identification consists of two parts, the identification of up-regulated and down-regulated genes in class 2 compared to class 1, respectively.

`pfp`	estimated percentage of false positive predictions (pfp) up to the position of each gene under two identificaiton each
`pval`	estimated pvalue for each gene being up- and down-regulated
`RSs`	Origina rank-sum (average rank) of each genes
`RSrank`	rank of the rank sum of each gene in ascending order
`Orirank`	original ranks in each comparison, which is used to compute rank sum
`AveFC`	fold change of average expression under class 1 over that under class 2, if multiple origin, than avraged across all origin. log-fold change if data is in log scaled, original fold change if data is unlogged.
`all.FC`	fold change of class 1/class 2 under each origin. log-fold change if data is in log scaled

Note

Percentage of false prediction (pfp), in theory, is equivalent of false discovery rate (FDR), and it is possible to be large than 1.

The function looks for up- and down- regulated genes in two seperate steps, thus two pfps are computed and used to identify gene that belong to each group.

The function is able to deal with single or multiple-orgin studies. It is similar to funcion RP.advance expect a rank sum is computed instead of rank product. This method is more sensitive to individual rank values, while rank product is more robust to outliers (refer RankProd vignette for details)

Author(s)

Fangxin Hong fhong@salk.edu

Examples

      
      #Suppose we want to check the consistence of the data 
      #sets generated in two different 
      #labs. For example, we would look for genes that were 
      # measured to be up-regulated in 
      #class 2 at lab 1, but down-regulated in class 2 at lab 2.
       data(arab)
      arab.cl2 <- arab.cl

      arab.cl2[arab.cl==0 &arab.origin==2] <- 1

      arab.cl2[arab.cl==1 &arab.origin==2] <- 0

      arab.cl2
  ##[1] 0 0 0 1 1 1 1 1 0 0


      #look for genes differentially expressed
      #between hypothetical class 1 and 2
      arab.sub=arab[1:500,] ##using subset for fast computation
      arab.gnames.sub=arab.gnames[1:500]
      Rsum.adv.out <- RSadvance(arab.sub,arab.cl2,arab.origin,
                          num.perm=100,
logged=TRUE,
                          gene.names=arab.gnames.sub,rand=123)

      attributes(Rsum.adv.out)

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)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(RankProd)
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/RankProd/RSadvance.Rd_%03d_medium.png", width=480, height=480)
> ### Name: RSadvance
> ### Title: Advanced Rank Sum Analysis of Microarray
> ### Aliases: RSadvance
> ### Keywords: htest
> 
> ### ** Examples
> 
>       
>       #Suppose we want to check the consistence of the data 
>       #sets generated in two different 
>       #labs. For example, we would look for genes that were 
>       # measured to be up-regulated in 
>       #class 2 at lab 1, but down-regulated in class 2 at lab 2.
>        data(arab)
>       arab.cl2 <- arab.cl
> 
>       arab.cl2[arab.cl==0 &arab.origin==2] <- 1
> 
>       arab.cl2[arab.cl==1 &arab.origin==2] <- 0
> 
>       arab.cl2
 [1] 0 0 0 1 1 1 1 1 0 0
>   ##[1] 0 0 0 1 1 1 1 1 0 0
> 
> 
>       #look for genes differentially expressed
>       #between hypothetical class 1 and 2
>       arab.sub=arab[1:500,] ##using subset for fast computation
>       arab.gnames.sub=arab.gnames[1:500]
>       Rsum.adv.out <- RSadvance(arab.sub,arab.cl2,arab.origin,
+                           num.perm=100,
+ logged=TRUE,
+                           gene.names=arab.gnames.sub,rand=123)
 The data is from  2 different origins 
 
Rank Sum analysis for two-class case 
 
Starting  100 permutations... 
Computing pfp... 
> 
>       attributes(Rsum.adv.out)
$names
[1] "pfp"     "pval"    "RSs"     "RSrank"  "Orirank" "AveFC"   "all.FC" 

>       
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>