heatdiagram              package:limma              R Documentation

_S_t_e_m_m_e_d _H_e_a_t _D_i_a_g_r_a_m

_D_e_s_c_r_i_p_t_i_o_n:

     Creates a heat diagram showing the co-regulation of genes under
     one condition with a range of other conditions.

_U_s_a_g_e:

     heatDiagram(results,coef,primary=1,names=NULL,treatments=colnames(coef),limit=NULL,orientation="landscape",low="green",high="red",cex=1,mar=NULL,ncolors=123,...)
     heatdiagram(stat,coef,primary=1,names=NULL,treatments=colnames(stat),critical.primary=4,critical.other=3,limit=NULL,orientation="landscape",low="green",high="red",cex=1,mar=NULL,ncolors=123,...)

_A_r_g_u_m_e_n_t_s:

 results: 'TestResults' matrix, containing elements -1, 0 or 1, from
          'decideTests'

    stat: numeric matrix of test statistics. Rows correspond to genes
          and columns to treatments or contrasts between treatments.

    coef: numeric matrix of the same size as 'stat'. Holds the
          coefficients to be displayed in the plot.

 primary: number or name of the column to be compared to the others.
          Genes are included in the diagram according to this column of
          'stat' and are sorted according to this column of 'coef'. If
          'primary' is a name, then 'stat' and 'coef' must have the
          same column names.

   names: optional character vector of gene names

treatments: optional character vector of treatment names

critical.primary: critical value above which the test statistics for
          the primary column are considered significant and included in
          the plot

critical.other: critical value above which the other test statistics
          are considered significant. Should usually be no larger than
          'critical.primary' although larger values are permitted.

   limit: optional value for 'coef' above which values will be plotted
          in extreme color. Defaults to 'max(abs(coef))'.

orientation: '"portrait"' for upright plot or '"landscape"' for plot
          orientated to be wider than high. '"portrait"' is likely to
          be appropriate for inclusion in printed document while
          '"landscape"' may be appropriate for a presentation on a
          computer screen.

     low: color associated with repressed gene regulation

    high: color associated with induced gene regulation

 ncolors: number of distinct colors used for each of up and down
          regulation

     cex: factor to increase or decrease size of column and row text

     mar: numeric vector of length four giving the size of the margin
          widths. Default is 'cex*c(5,6,1,1)' for landscape and
          'cex*c(1,1,4,3)' for portrait.

     ...: any other arguments will be passed to the 'image' function

_D_e_t_a_i_l_s:

     Users are encouraged to use 'heatDiagram' rather than
     'heatdiagram' as the later function may be removed in future
     versions of limma.

     This function plots an image of gene expression profiles in which
     rows (or columns for portrait orientation) correspond to treatment
     conditions and columns (or rows) correspond to genes. Only genes
     which are significantly differentially expressed in the primary
     condition are included. Genes are sorted by differential
     expression under the primary condition.

     Note: the plot produced by this function is unique to the limma
     package. It should not be confused with "heatmaps" often used to
     display results from cluster analyses.

_V_a_l_u_e:

     An image is created on the current graphics device. A matrix with
     named rows containing the coefficients used in the plot is also
     invisibly returned.

_A_u_t_h_o_r(_s):

     Gordon Smyth

_S_e_e _A_l_s_o:

     'image'.

_E_x_a_m_p_l_e_s:

     library(sma)
     data(MouseArray)
     MA <- normalizeWithinArrays(mouse.data,layout=mouse.setup)
     design <- cbind(c(1,1,1,0,0,0),c(0,0,0,1,1,1))
     fit <- lmFit(MA,design=design)
     contrasts.mouse <- cbind(Control=c(1,0),Mutant=c(0,1),Difference=c(-1,1))
     fit <- eBayes(contrasts.fit(fit,contrasts=contrasts.mouse))
     results <- decideTests(fit,method="global",p=0.1)
     heatDiagram(results,fit$coef,primary="Difference")

