hmap.eset             package:simpleaffy             R Documentation

_D_r_a_w _a _h_e_a_t_m_a_p _f_r_o_m _a_n _A_f_f_y_B_a_t_c_h _o_b_j_e_c_t

_D_e_s_c_r_i_p_t_i_o_n:

     Given either an 'AffyBatch' draw a heatmap.

_U_s_a_g_e:

     hmap.eset(x,probesets,samples=1:length(sampleNames(x)),scluster=standard.pearson,pcluster=standard.pearson,slabs=sampleNames(x)[samples],plabs,col="bwr",min.val=NULL ,max.val=NULL,scale=FALSE,spread=6,by.fc=F,sdev=NULL,show.legend=T,title=NULL,cex=0.5)

_A_r_g_u_m_e_n_t_s:

       x: The 'AffyBatch' object to get the expression data from 

probesets: What probesets to plot, defaults to all of them 

 samples: Which samples to plot 

scluster: The function to use to cluster the samples by. Can also be a
          dendrogram object. 

pcluster: The function to use to cluster the probesets by. Can also be
          a dendrogram object. 

   slabs: Labels for the sample axis 

   plabs: Labels for the probeset axis defaults to geneNames(x) 

     col: Vector of colour values to use (see below) 

 min.val: The minimum intensity to plot 

 max.val: The maximum intensity to plot 

   scale: Scale each gene's clouring based on standard deviation (See
          below)

  spread: If the data is scaled, how many standard deviations (or fold
          changes) either way should we show. If no scaling, then does
          nothing 

   by.fc: If the data is scaled, scale by s.d. or by fold.change? 

    sdev: A vector of standard deviaitions for each gene to be plotted.
          If no value is supplied these are worked out from the data.

show.legend: Draw a scale on the graph and show the title if supplied

   title: The title of the graph

     cex: Character expansion

_D_e_t_a_i_l_s:

     Takes an 'AffyBatch' object and plots a heatmap. At its simplest,
     all that is required is an AffyBatch object (as calculated by
     'call.exprs') and a vector supplying the probesets to plot. These
     can be specified by name, as an integer index or as a vector of
     TRUEs and FALSES. The function will try to do something sensible
     with the labels. If it fails you will need to specify this with
     plabs. The function will then draw a heatmap, coloured
     blue-white-red in increasing intensity, scaled so that 100

     Col can be used to change the colouring. "bwr" specifies
     blue-white-red, "rbg" specifies red-black-green, and "ryw"
     specifies red-yellow-white. Alternatively, a vector of arbitrary
     colours can be supplied (try 'rainbow(21)', for example).

     The clustering method can also be changed by supplying, either, a
     function that takes a matrix of expression values and returns an
     'hclust' or 'dendrogram' object, or alternatively, an 'hclust' or
     'dendrogram' object itself. Setting either of these to NULL will
     stop the heatmap being clustered on that axis.

     Scaling is somewhat more complex. If scale is TRUE, then each gene
     is coloured independently, on a scale based on its standard
     deviation. By default this is calculated for the samples that are
     being plotted, unless a value is supplied for sdev - in which case
     this should be a vector of standard deviations, one for each
     probeset being plotted (and in the same order). This scaling is
     done after the clustering. For more details on how all of this
     works see the website <URL:
     http://bioinf.picr.man.ac.uk/simpleaffy> and also look at
     'hmap.pc' which uses the scaling to plot transcripts identified as
     being differentially expressed.

_V_a_l_u_e:

     Returns an (invisible) list containing the dendrograms used for
     samples and probesets

_A_u_t_h_o_r(_s):

     Crispin J Miller

_S_e_e _A_l_s_o:

     'hmap.pc' 'blue.white.red.cols' 'standard.pearson'

_E_x_a_m_p_l_e_s:

       ## Not run: 
      
         eset.mas <- call.exprs(eset,"mas5")
         hmap.eset(eset.mas,1:100,1:6,col="rbg")
       
     ## End(Not run)