RankProd (version 2.44.0)

RSadvance: 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

See Also

topGene RP plotRP RPadvance

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)