permax {permax}R Documentation

2-sample permutation t-tests for high dimensional data

Description

For high dimensional vectors of observations, computes t statistics for each attribute, and assesses significance using the permutation distribution of the maximum and minimum over all attributes.

Usage

permax(data, ig1, nperm=0, logs=TRUE, ranks=FALSE, min.np=1, ig2, WHseed=NULL)

Arguments

data Data matrix or data frame. Each case is a column, and each row is an attribute (the opposite of the standard configuration).
ig1 The columns of data corresponding to group 1
nperm The number of random permutations to use in computing the p-values. The default is to use the entire permutation distribution, which is only feasible if the sample sizes are fairly small
logs If logs=TRUE (the default), then logs of the values in data are used in the statistics.
ranks If ranks=T, then within row ranks are used in place of the values in data in the t statistics. This is equivalent to using the Wilcoxon statistic. Default is ranks=F
min.np data will be subset to only rows with at least min.np values larger than min(data) in the columns in ig1 and ig2
ig2 The columns of data corresponding to group 2. The default is to include all columns not in ig1 in group 2. When both ig1 and ig2 are given, columns not in either are excluded from the tests.
WHseed Initial random number seed (a vector of 3 integers). If missing, an initial seed is generated from the runif() function. Not needed if all permutations are calculated.

Details

For DNA array data, this function is designed to identify the genes which best discriminate between two tissue types. 2-sample t statistics are computed for each gene using logs (default), raw values, or ranks. Upper and lower p-values (p.upper, p.lower) are computed by comparing each statistic to the permutation distribution of the maximum and minimum (largest negative) statistic over all genes. The pind component of the output gives the p-value for the permutation distribution of each individual gene.

It is strongly recommended that different seeds be used for different runs, and ideally the final seed from one run, attr(output,'seed.end'), would be used as the initial seed in the next run.

Value

Output is a data.frame of class 'permax', with columns stat: the standardized test statistics for each row pind: individual permutation p-values (2-sided) p2: 2-sided p-value using the distribution of the max overall rows p.lower: 1-sided p-value for lower levels in group 1 p.upper: 1-sided p-value for higher levels in group 1 nml: # permutations where this row was the most significant for p.lower nmr: # permutations where this row was the most sig for p.upper m1, m2: means of groups 1 and 2 (means of logs if logs=T) s1, s2: std deviations of groups 1 and 2 (of logs if logs=T) np1,np2: # values > min(data) in groups 1 and 2 mdiff: difference of means (if logs=T the difference of geometric means) mrat: ratio of means (if logs=T ratio of geometric means)
Also, if nperm>0, then output includes attributes 'seed.start' giving the initial random number seed, and 'seed.end' giving the value of the seed at the end. These can be accessed with the attributes() and attr() functions.

See Also

summary.permax, plot.permax, permcor, permsep.

Examples

#generate make believe data
   set.seed(1292)
   ngenes <- 1000
   m1 <- rnorm(ngenes,4,1)
   m2 <- rnorm(ngenes,4,1)
    exp1 <- cbind(matrix(exp(rnorm(ngenes*5,m1,1)),nrow=ngenes),
               matrix(exp(rnorm(ngenes*10,m2,1)),nrow=ngenes))
   exp1[exp1<20] <- 20
   sub <- exp1>20 & exp1<150
   exp1[sub] <- ifelse(runif(length(sub[sub]))<.5,20,exp1[sub])
   dimnames(exp1) <- list(paste('x',format(1:ngenes,justify='l'),sep=''),
                     paste('sample',format(1:ncol(exp1),justify='l'),sep=''))
   dimnames(exp1) <- list(paste('x',1:ngenes,sep=''),
                     paste('sample',1:ncol(exp1),sep=''))
   exp1 <- round(exp1)

   uu <- permax(exp1,1:5)
  summary(uu,nl=5,nr=5) # 5 most extreme in each direction

[Package permax version 1.2.1 Index]