safeplot                package:safe                R Documentation

_S_A_F_E _p_l_o_t

_D_e_s_c_r_i_p_t_i_o_n:

     A SAFE plot for a given category displays the empirical
     distribution function for the ranked local statistics of a given
     category.

_U_s_a_g_e:

     safeplot(safe)
     safeplot(safe , cat.name)
     safeplot(c.vec=, local.stats= , p.val=, one.sided=, limits=,
              extreme=, italic =, x.label=)

_A_r_g_u_m_e_n_t_s:

    safe: Object of class 'SAFE'. 

cat.name: Name of the category to be plotted. If omitted, the most
          significant category is plotted. 

   c.vec: Optional logical vector specifying membership to a gene
          category. 

local.stats: Optional numeric vector of local statistics. Gene names
          should  be provided as 'names(local.stats)'.

   p.val: Optional numeric value of the category's empirical p-value

one.sided: Optional logical value indicating if local statistics are
          one-sided. 

  limits: Limits of the shaded region in the plot on the unranked
          scale. 

 extreme: Optional logical value whether only genes in the shaded
          region should be labeled. 

  italic: Optional logical value whether gene names should be italic. 

 x.label: Character string for the x-axis label. 

_D_e_t_a_i_l_s:

     SAFE-plots are suggested as appropriate for visualizing the
     differential expression in a given category relative to the
     complementary set of genes. The empirical cumulative distribution
     is plotted for the ranked local statistics in the category. Tick
     marks are drawn along the top of the graph to indicate each gene's
     positions, and labeled when sufficient space permits. In this
     manner, genes with the most extreme local statistics can be
     identified as contributing to a categories significance.

_A_u_t_h_o_r(_s):

     William T. Barry: bill.barry@duke.edu

_R_e_f_e_r_e_n_c_e_s:

     W. T. Barry, A. B. Nobel and F.A. Wright, 2005, _Significance
     Analysis of functional categories in gene expression studies: a
     structured permutation approach_, _Bioinformatics_ {\bf 21}(9)
     1943-1949. 

     See also the vignette included with this package.

_S_e_e _A_l_s_o:

     {'safe'.}

_E_x_a_m_p_l_e_s:

     ## Simulate a dataset with 1000 genes and 20 arrays in a 2-sample design.
     ## The top 100 genes will be differentially expressed at varying levels

     g.alt <- 100
     g.null <- 900
     n <- 20

     data<-matrix(rnorm(n*(g.alt+g.null)),g.alt+g.null,n)
     data[1:g.alt,1:(n/2)] <- data[1:g.alt,1:(n/2)] + 
                              seq(2,2/g.alt,length=g.alt)
     dimnames(data) <- list(c(paste("Alt",1:g.alt),
                              paste("Null",1:g.null)),
                            paste("Array",1:n))

     ## A treatment vector 
     trt <- rep(c("Trt","Ctr"),each=n/2)

     ## 2 alt. categories and 18 null categories of size 50

     C.matrix <- kronecker(diag(20),rep(1,50))
     dimnames(C.matrix) <- list(dimnames(data)[[1]],
         c(paste("TrueCat",1:2),paste("NullCat",1:18)))
     dim(C.matrix)

     results <- safe(data,trt,C.matrix,Pi.mat = 100)
     results

     ## SAFE-plot made for the first category
     if (interactive()) { 
     safeplot(results,"TrueCat 1")
     }

