hmap.pc              package:simpleaffy              R Documentation

_D_r_a_w _a _h_e_a_t_m_a_p _f_r_o_m _a_n _P_a_i_r_C_o_m_p _o_b_j_e_c_t

_D_e_s_c_r_i_p_t_i_o_n:

     Given either a 'PairComp' object draw a heatmap.

_U_s_a_g_e:

     hmap.pc(x,eset,samples=rownames(pData(x)),scluster=standard.pearson,pcluster=standard.pearson,slabs,plabs,col="rbg",scale=T,spread=10,by.fc=F,gp=group(x),mbrs=members(x),show.legend=T,title=NULL,cex=0.1)

_A_r_g_u_m_e_n_t_s:

       x: The 'PairComp' object to get the probeset list (and other
          data) from

    eset: The 'AffyBatch' object containing expression data 

 samples: 

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 

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

   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, do it by fold change? 

      gp: The column in the expression set's pData object used to
          select the samples to plot. By default this is the one used
          to calculate x. 

    mbrs: The members of the 'group' column that we wish to plot. By
          default these are the pair used to calculate x. If 'all' is
          supplied then all samples are used.

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 a 'PairComp' object and an 'AffyBatch' object and plots a
     heatmap. At its simplest, all that is required are these two
     objects. The function will then draw a heatmap, coloured
     red-black-green in increasing intensity, scaled for each gene
     based on standard deviation. The legend shows how these colours
     translate into intensity. 

     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).

     Scaling is somewhat complex. If scale is TRUE, then each gene is
     coloured independently, on a scale based on its standard
     deviation. This is calculated as follows: 'group' supplies a
     column in the pData object of 'eset' that is used to collect
     samples together (generally as replicate groups). 'members'
     supplies the entries within this column that are to be used.
     (Unless specified, the function uses the same value for 'group'
     and 'members' used to calculate the PairComp object). The function
     uses these data to calculate the standard deviation for each
     probeset within each set of replicates, and then calculates the
     average sd for each gene. This is then used to scale the data so
     that each probeset is plotted on a scale that shows the number of
     standard deviations away from the mean it is for that sample. For
     more details on how all of this works see the website <URL:
     http://bioinf.picr.man.ac.uk/simpleaffy>.

     Alternatively, by setting by.fc to FALSE, scaling can be done
     simply in terms of fold-change, in which case, spread defines the
     maximum and minimum fold changes to show.

_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.eset' 'blue.white.red.cols' 'standard.pearson'

_E_x_a_m_p_l_e_s:

       ## Not run: 
         pc <- pairwise.comparison(eset.mas,group="group",members=c("a","b"),spots=eset)
         pf <- pairwise.filter(pc)
         hmap.pc(pf,eset.mas)
       
     ## End(Not run)