plotFreqStat              package:aCGH              R Documentation

_f_r_e_q_u_e_n_c_y _p_l_o_t_s _a_n_d _s_i_g_n_i_f_i_c_a_n_c_e _a_n_a_l_y_s_i_s

_D_e_s_c_r_i_p_t_i_o_n:

     The main application of this function is to plot the frequency of
     changes.

_U_s_a_g_e:

     plotFreqStat(aCGH.obj, resT = NULL, pheno = rep(1, ncol(aCGH.obj)),
                  chrominfo = human.chrom.info.Jul03,
                  X = TRUE, Y = FALSE,
                  rsp.uniq = unique(pheno),
                  all = length(rsp.uniq) == 1 && is.null(resT),
                  titles = if (all) "All Samples" else rsp.uniq,
                  cutplot = 0, thres = .25, factor = 2.5, ylm = c(-1, 1),
                  p.thres = c(.01, .05, .1), numaut = 22, onepage = TRUE,
                  colored = TRUE)

_A_r_g_u_m_e_n_t_s:

aCGH.obj: Object of class 'aCGH'

    resT: Data frame having the same structure as the result of
          applying 'mt.maxT' or 'mt.minP' functions from Bioconductor's
          'multtest' package for multiple testing. The result is a data
          frame including the following 4 components: 'index',
          'teststat', 'rawp' and 'adjp'. 

   pheno: phenotype to compare.

chrominfo: Chromosomal information. Defaults to
          'human.chrom.info.Jul03' 

       X: Include X chromosome? Defaults to yes.

       Y: Include Y chromosome? Defaults to no.

rsp.uniq: 'rsp.uniq' specified the codes for the groups of interest.
          Default is the unique levels of the phenotype. Not used when
          'all' is T. 

     all: 'all' specifies whether samples should be analyzed by
          subgroups (T) or together (F). 

  titles: 'titles' names of the groups to be used. Default is the
          unique levels of the 'pheno'. 

 cutplot: only clones with at least 'cutplot' frequency of gain and
          loss are plotted. 

   thres: 'thres' is either a vector providing unique threshold for
          each sample or a vector of the same length as number of
          samples (columns in 'data') providing sample-specific
          threshold. If 'aCGH.obj' has non-null sd.samples, then
          'thres' is automatically replaced by 'factor' times madGenome
          of 'aCGH' object. Clone is considered to be gained if it is
          above the threshold and lost if it below negative threshold.
          Used for plotting the gain/loss frequency data as well as for
          clone screening and for significance analysis when
          'threshold' is TRUE.Defaults to 0.25 

  factor: 'factor' specifies the number by which experimental
          variability should be multiplied. used only when
          sd.samples('aCGH.obj') is not NULL or when factor is greater
          than 0. Defaults to 2.5

     ylm: 'ylm' vertical limits for the plot

 p.thres: 'p.thres' vector of p-value ciut-off to be plotted. computed
          conservatively as the threshold corresponding to a given
          adjusted p-value. 

  numaut: 'numaut' number of the autosomes

 onepage: 'onepage' whether all plots are to be plotted on one page or
          different pages. When more than 2 groups are compared, we
          recommend multiple pages. 

 colored: Is plotting in color or not? Default is TRUE.

_E_x_a_m_p_l_e_s:

     data(colorectal)

     ## Use mt.maxT function from multtest package to test
     ## differences in group means for each clone grouped by sex
     colnames(phenotype(colorectal))
     sex <- phenotype(colorectal)$sex
     sex.na <- !is.na(sex)
     colorectal.na <- colorectal[ ,sex.na, keep = TRUE ]
     dat <- log2.ratios.imputed(colorectal.na)
     resT.sex <- mt.maxT(dat, sex[sex.na], test = "t", B = 1000)

     ## Plot the result along the genome
     plotFreqStat(colorectal.na, resT.sex, sex[sex.na],
                  titles = c("Male", "Female"))

     ## Adjust the p.values from previous exercise with "fdr"
     ## method and plot them
     resT.sex.fdr <- resT.sex
     resT.sex.fdr$adjp <- p.adjust(resT.sex.fdr$rawp, "fdr")
     plotFreqStat(colorectal.na, resT.sex.fdr, sex[sex.na],
                  titles = c("Male", "Female"))

     ## Derive statistics and p-values for testing the linear association of
     ## age with the log2 ratios of each clone along the samples

     age <- phenotype(colorectal)$age
     age.na <- which(!is.na(age))
     age <- age[age.na]
     colorectal.na <- colorectal[, age.na]
     stat.age <- aCGH.test(colorectal.na, age, test = "linear.regression", p.adjust.method = "fdr")

     #separate into two groups: < 70 and > 70 and plot freqeuncies of gain and loss
     #for each clone. Note that statistic plotted corresponds to linear coefficient
     #for age variable

     plotFreqStat(colorectal.na, stat.age, ifelse(age < 70, 0, 1), titles =
                  c("Young", "Old"), X = FALSE, Y = FALSE)

