RPadvance: Advanced Rank Product Analysis
In RankProd: Rank Product method for identifying differentially expressed genes with application in meta-analysis

Description Usage Arguments Value Note Author(s) References See Also Examples

The function performs the Rank Product method to identify differentially expressed genes. It is possible to do either a one-class or two-class analysis. It is also possible to combine data from different studies (e.g. datasets generated by different laboratories. This function has been kept only to guarantee backward compatibility, in fact the same results can be obtained by RankProducts.

1 2	RPadvance(data, cl, origin, num.perm = 100, logged = TRUE, na.rm = TRUE, gene.names = NULL, plot = FALSE, rand = NULL, huge = FALSE)

`data`	the data set that should be analyzed. Every row of this dataset 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 class data, the label for each sample should be 1
`origin`	a vector containing the origin labels of the samples. The label is the same for samples within one lab and different for samples from different labs.
`num.perm`	in this version of the package, this parameter is not used any more, but it is kept for backward compatibility
`logged`	if "TRUE" data have been previously log transformed. Otherwise it should be set as "FALSE"
`na.rm`	if "FALSE", the NA value will not be used in computing rank. If "TRUE" (default), the missing values will be replaced by the genewise median of the non-missing values. Gene with a number of missing values greater than 50% are still not considered in the analysis
`gene.names`	if "NULL", no gene name will be attached to the outputs, otherwise it contains the vector of gene names
`plot`	if "TRUE", plot the estimated pfp vs the rank of each gene
`rand`	if specified, the random number generator will be put in a reproducible state
`huge`	if "TRUE" not all the outputs are evaluated in order to save space

A summary of the results obtained by the Rank Product method.

`pfp`	estimated percentage of false positive predictions (pfp), both considering upregulated an downregulated genes
`pval`	estimated pvalues per each gene being up- and down-regulated
`RPs`	the Rank Product statistics evaluated per each gene
`RPrank`	rank of the Rank Product of each gene in ascending order
`Orirank`	ranks obtained when considering each possible pairing. In this version of the package, this is not used to compute Rank Product (or Rank Sum), but it is kept for backward compatibility
`AveFC`	fold changes of average expressions (class1/class2). log fold-change if data has been log transformed, original fold change otherwise
`allrank1`	fold change of class 1/class 2 under each origin. log fold-change if data has been log transformed, original fold change otherwise
`allrank2`	fold change of class 2/class 1 under each origin. log fold-change if data has been log transformed, original fold change otherwise
`nrep`	total number of replicates
`groups`	vector of labels (as cl)

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 replace function RP in the same library. it is a more general version, as it is able to handle data from differnt origins.

Francesco Del Carratore, francesco.delcarratore@postgrad.manchester.ac.uk
Andris Janckevics, andris.jankevics@gmail.com

Breitling, R., Armengaud, P., Amtmann, A., and Herzyk, P.(2004) Rank Products: A simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments, FEBS Letter, 57383-92

topGene RP RSadvance plotRP RP.advance RankProducts

# 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)

##For data with single origin
subset <- c(1:4,28:30)
origin <- rep(1,7)
#identify genes 
RP.out <- RPadvance(golub[,subset],golub.cl[subset],
        origin,plot=FALSE,rand=123)
      
#For data from multiple origins
      
#Load the data arab in the package, which contains 
# the expression of 22,081 genes
# of control and treatment group from the experiments 
#indenpently conducted at two 
#laboratories.
data(arab)
arab.origin #1 1 1 1 1 1 2 2 2 2
arab.cl #0 0 0 1 1 1 0 0 1 1
RP.adv.out <- RPadvance(arab,arab.cl,arab.origin,
                num.perm=100,gene.names=arab.gnames,logged=TRUE,rand=123)

attributes(RP.adv.out)
head(RP.adv.out$pfp)
head(RP.adv.out$RPs)
head(RP.adv.out$AveFC)

Loading required package: Rmpfr
Loading required package: gmp

Attaching package: 'gmp'

The following objects are masked from 'package:base':

    %*%, apply, crossprod, matrix, tcrossprod

C code of R package 'Rmpfr': GMP using 64 bits per limb


Attaching package: 'Rmpfr'

The following objects are masked from 'package:stats':

    dbinom, dnorm, dpois, pnorm

The following objects are masked from 'package:base':

    cbind, pmax, pmin, rbind

 The data is from  1 different origins 
 
Rank Product analysis for two-class case 
 
Rank Product analysis for unpaired case 
 

 done   [1] 1 1 1 1 1 1 2 2 2 2
 [1] 0 0 0 1 1 1 0 0 1 1
 The data is from  2 different origins 
 
Rank Product analysis for two-class case 
 
Rank Product analysis for unpaired case 
 
Rank Product analysis for unpaired case 
 

 done  $names
 [1] "RPs"               "RPrank"            "pfp"              
 [4] "pval"              "AveFC"             "groups"           
 [7] "RandomPairs_ranks" "nrep"              "allrank1"         
[10] "allrank2"          "Orirank"          

          class1 < class2 class1 > class2
244901_at       1.0677795        1.049335
244902_at       1.0868398        1.036828
244903_at       1.1766396        1.061817
244904_at       1.0832921        1.180194
244905_at       1.0667289        1.076800
244906_at       0.9659401        1.046720
          class1 < class2 class1 > class2
244901_at       248.39049        166.5156
244902_at       225.03952        247.2433
244903_at       159.07172        175.0358
244904_at       228.57455        128.4567
244905_at       247.09183        191.5251
244906_at        96.46004        362.4832
                  [,1]
244901_at  0.060713984
244902_at -0.005148036
244903_at -0.008684345
244904_at  0.128771916
244905_at  0.030076779
244906_at -0.217625131