heatplot                package:made4                R Documentation

_D_r_a_w_s _h_e_a_t_m_a_p _w_i_t_h _d_e_n_d_r_o_g_r_a_m_s.

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

     'heatplot' calls 'heatmap' using a red-green colour scheme by
     default.  It also draws dendrograms of the cases and variables
     using correlation similarity metric and average linkage clustering
     as described by Eisen. 'heatplot' is useful for a quick overview
     or exploratory analysis of data

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

     heatplot(dataset, dend = TRUE, lowcol = "green", 
                     highcol = "red", Colv=NULL, Rowv=NULL, ...)

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

 dataset: a 'matrix', 'data.frame',  'exprSet' or 'marrayRaw'.   If the
          input is gene expression data in a 'matrix' or 'data.frame'.
          The  rows and columns are expected to contain the variables
          (genes) and cases (array samples)  respectively. 

    dend: Logical, indicating whether dendrograms should be drawn.
          Default is TRUE. If FALSE both Colv and Rowv are set to NA.

Colv, Rowv: Vector or object of class 'dendrogram' used to reorder the
          columns, or rows.  If no ordering is  required, set Colv or
          Rowv = NA.  The default is NULL

lowcol, highcol: Character indicating colours to be used for down and
          upregulated genes when drawing heatmap. Default is
          lowcol="green", and highcol="red".

     ...: further arguments passed to or from other methods 

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

     The hierarchical plot is produced using average linkage cluster
     analysis with a correlation metric distance.  'heatplot' calls
     'heatmap' and  'dendrogram'.

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

_N_o_t_e:

     Because Eisen et al., 1998 use green-red colours for the heatmap
     'heatplot'  uses these by default however a blue-red or
     yellow-blue are easily obtained by  changing lowcol and highcol

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

     Aedin Culhane

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

     Eisen MB, Spellman PT, Brown PO and Botstein D. (1998). Cluster
     Analysis and Display of  Genome-Wide Expression Patterns. Proc
     Natl Acad Sci USA 95, 14863-8.

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

     See also as 'hclust',  'heatmap' and 'dendrogram'

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

     data(khan)

     heatplot(khan$train[1:30,], lowcol="blue", highcol="red")
     heatplot(khan$train[1:26,], lowcol="blue", highcol="red", 
              labRow = c(64:1), labCol=LETTERS[1:26])

     if (require(ade4, quiet = TRUE)) {
     khan.coa<-dudi.coa(khan$train, scan=FALSE, nf=6)
     }

     # Provides a view of the components of the Correspondence analysis 
     heatplot(khan.coa$li, dend=FALSE)   

     # transposed so that it is easier to view. Can see that the difference between tissues 
     # and cell line samples are defined in the first axis.

     heatplot(t(khan.coa$li), dend=FALSE, lowcol="blue") 

