pickgene              package:pickgene              R Documentation

_P_l_o_t _a_n_d _P_i_c_k _G_e_n_e_s _b_a_s_e_d _o_n _D_i_f_f_e_r_e_n_t_i_a_l _E_x_p_r_e_s_s_i_o_n

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

     The function picks plots the average intensity versus linear
     contrasts (currently linear, quadratic up to cubic) across
     experimental conditions. Critical line is determine according to
     Bonferroni-like multiple comparisons, allowing SD to vary with
     intensity.

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

     pickgene(data, geneID = 1:nrow(data), overalllevel = 0.05,
              npickgene = -1, marginal = FALSE, rankbased = TRUE,
              allrank = FALSE, meanrank = FALSE, offset = 0,
              modelmatrix = model.pickgene(faclevel, facnames,
              contrasts.fac, collapse, show, renorm), faclevel =
              ncol(data), facnames =
              letters[seq(length(faclevel))], contrasts.fac =
              "contr.poly", show = NULL, main = "", renorm = 1,
              drop.negative = FALSE, plotit = npickgene < 1, mfrow
              = c(nr, nc), mfcol = NULL, ylab = paste(shownames,
              "Trend"), ...)

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

    data: data matrix

  geneID: gene identifier (default '1:nrow(x)')

overalllevel: overall significance level (default '0.05')

npickgene: number of genes to pick (default '-1' allows automatic
          selection)

marginal: additive model if TRUE, include interactions if FALSE

rankbased: use ranks if TRUE, log tranform if FALSE

 allrank: rank all chips together if true, otherwise rank separately

meanrank: show mean abundance as rank if TRUE

  offset: offset for log transform

modelmatrix: model matrix with first row all 1's and other rows
          corresponding to design contrasts; automatically created by
          call to 'model.pickgene' if omitted

faclevel: number of factor levels for each factor

facnames: factor names

contrasts.fac: type of contrasts

    show: vector of contrast numbers to show (default is all)

    main: vector of main titles for plots (default is none)

  renorm: vector to renormalize contrasts (e.g. use 'sqrt(2)' to turn
          two-condition contrast into fold change)

drop.negative: drop negative values in log transform

  plotit: plot if TRUE

   mfrow: 'par()' plot arrangement by rows (default up to 6 per page;
          set to NULL to not change)

   mfcol: 'par()' plot arrangement by columns (default is NULL)

    ylab: vertical axis labels

     ...: parameters for 'robustscale'

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

     Infer genes that differentially express across conditions using a
     robust data-driven method. Adjusted gene expression levels 'A' are
     replaced by 'qnorm(rank(A))', followed by 'robustscale' estimation
     of center and spread. Then Bonferroni-style gene by gene tests are
     performed and displayed graphically.

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

     Data frame containing significant genes with the following
     information: 

    pick: data frame with picked genes

   score: data frame with center and spread for plotting

   probe: gene identifier

 average: average gene intensity

   fold1: positive fold change

   fold2: negative fold change

  pvalue: Bonferroni-corrected p-value

       x: mean expression level (antilog scale)

       y: contrast (antilog scale)

  center: center for contrast

   scale: scale (spread) for contrast

   lower: lower test limit

   upper: upper test limit

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

     Yi Lin and Brian Yandell

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

     Y Lin, BS Yandell and ST Nadler (2000) ``Robust Data-Driven
     Inference for Gene Expression Microarray Experiments,'' Technical
     Report, Department of Statistics, UW-Madison.

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

     'pickgene'

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

     ## Not run: 
     pickgene( data )
     ## End(Not run)

