ebplots               package:EBarrays               R Documentation

_V_a_r_i_o_u_s _p_l_o_t_t_i_n_g _r_o_u_t_i_n_e_s _i_n _t_h_e _E_B_a_r_r_a_y_s _p_a_c_k_a_g_e

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

     Various plotting routines, used for diagnostic purposes

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

     checkCCV(data, useRank = FALSE, f = 1/2)
     checkModel(data, fit, model = c("gamma", "lognormal", "lnnmv"),
                number = 9, nb = 10, cluster = 1, groupid = NULL)
     checkVarsQQ(data, groupid, ...)
     checkVarsMar(data, groupid, xlab, ylab, ...)
     plotMarginal(fit, data, kernel = "rect", n = 100,
                  bw = "nrd0", adjust = 1, xlab, ylab,...)
     plotCluster(fit, data, cond = NULL, ncolors = 123, sep=TRUE,
                 transform=NULL)

     ## S3 method for class 'ebarraysEMfit':
     plot(x, data, plottype="cluster", ...)

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

    data: data, as a ``matrix'' or ``ExpressionSet''

 useRank: logical. If 'TRUE', ranks of means and c.v.-s are used in the
          scatterplot 

       f: passed on to 'lowess'

  fit, x: object of class ``ebarraysEMfit'', typically produced by a
          call to 'emfit' 

   model: which theoretical model use for Q-Q plot. Partial string
          matching is allowed 

  number: number of bins for checking model assumption.

      nb: number of data rows included in each bin for checking model
          assumption

 cluster: check model assumption for data in that cluster 

 groupid: an integer vector indicating which group each sample belongs
          to. groupid for samples not included in the analysis should
          be 0. 

kernel, n, bw, adjust: passed on to 'density'

    cond: a vector specifying the condition for each replicate

 ncolors: different number of colors in the plot

xlab, ylab: labels for x-axis and y-axis

     sep: whether or not to draw horizontal lines between clusters

transform: a function to transform the original data in plotting

plottype: a character string specifying the type of the plot. Available
          options are "cluster" and "marginal". The default plottype
          "cluster" employs function 'plotCluster' whereas the
          "marginal" plottype uses function 'plotMarginal'.

     ...: extra arguments are passed to the 'qqmath', 'histogram' and
          'xyplot' call used to produce the final result 

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

     'checkCCV' checks the constant coefficient of variation assumption
     made in the GG and LNN models. 'checkModel' generates QQ plots for
     subsets of (log) intensities in a small window. They are used to
     check the Log-Normal assumption on observation component of the
     LNN and LNNMV models and the Gamma assumption on observation
     component of the GG model. 'checkVarsQQ' generates QQ plot for
     gene specific sample variances. It is used to check the assumption
     of a scaled inverse chi-square prior on gene specific variances,
     made in the LNNMV model. 'checkVarsMar' is another diagnostic tool
     to check this assumption. The density histogram of gene specific
     sample variances and the density of the scaled inverse chi-square
     distribution with parameters estimated from data will be plotted.
     'checkMarginal' generates predictive marginal distribution from
     fitted model and compares with estimated marginal (kernel) density
     of data. Available for the GG and LNN models only.  'plotCluster'
     generate heatmap for gene expression data with clusters

_V_a_l_u_e:

     'checkModel', 'checkVarsQQ' and 'checkVarsMar' return an object of
     class ``trellis'', using function in the Lattice package. Note
     that in certain situations, these may need to be explicitly
     `print'-ed to have any effect.

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

     Ming Yuan, Ping Wang, Deepayan Sarkar, Michael Newton, and
     Christina Kendziorski

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

     Newton, M.A., Kendziorski, C.M., Richmond, C.S., Blattner, F.R.
     (2001). On differential variability of expression ratios:
     Improving statistical inference about gene expression changes from
     microarray data. Journal of Computational Biology 8:37-52.

     Kendziorski, C.M., Newton, M.A., Lan, H., Gould, M.N. (2003). On
     parametric empirical Bayes methods for comparing multiple groups
     using replicated gene expression profiles. Statistics in Medicine
     22:3899-3914.

     Newton, M.A. and Kendziorski, C.M. Parametric Empirical Bayes
     Methods for Microarrays in The analysis of gene expression data:
     methods and software. Eds. G. Parmigiani, E.S. Garrett, R.
     Irizarry and S.L. Zeger, New York: Springer Verlag, 2003.

     Newton, M.A., Noueiry, A., Sarkar, D., and Ahlquist, P. (2004).
     Detecting differential gene expression with a semiparametric
     hierarchical mixture model. Biostatistics 5: 155-176.

     Yuan, M. and Kendziorski, C. (2006). A unified approach for
     simultaneous gene clustering and differential expression
     identification. Biometrics 62(4): 1089-1098.

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

     'emfit', 'lowess'

