hierM               package:maigesPack               R Documentation

_F_u_n_c_t_i_o_n _t_o _d_o _h_i_e_r_a_r_c_h_i_c_a_l _c_l_u_s_t_e_r _a_n_a_l_y_s_i_s

_D_e_s_c_r_i_p_t_i_o_n:

     This is a function to do hierarchical clustering analysis for
     objects of classes 'maiges', 'maigesRaw' and 'maigesANOVA'. Use
     the function 'hierMde' for objects of class 'maigesDEcluster'.

_U_s_a_g_e:

     hierM(data, group=c("C", "R", "B")[1], distance="correlation",
           method="complete", doHeat=TRUE, sLabelID="SAMPLE",
           gLabelID="GeneName", rmGenes=NULL, rmSamples=NULL,
           rmBad=TRUE, geneGrp=NULL, path=NULL, ...)

_A_r_g_u_m_e_n_t_s:

    data: object of class 'maigesRaw', 'maiges', 'maigesANOVA' or
          'maigesDEcluster'.

   group: character string giving the type of grouping: by rows 'R',
          columns 'C' (default) or both 'B'.

distance: char string giving the type of distance to use. Here we use
          the function 'Dist' and the possible values are 'euclidean',
          'maximum', 'manhattan', 'canberra', 'binary', 'pearson',
          'correlation' (default) and 'spearman'.

  method: char string specifying the linkage method for the
          hierarchical cluster. Possible values are 'ward', 'single',
          'complete' (default), 'average', 'mcquitty', 'median' or
          'centroid'

  doHeat: logical indicating to do or not the heatmap. If FALSE, only
          the dendrogram is displayed.

sLabelID: character string specifying the sample label ID to be used to
          label the samples.

gLabelID: character string specifying the gene label ID to be used to
          label the genes.

 rmGenes: char list specifying genes to be removed.

rmSamples: char list specifying samples to be removed.

   rmBad: logical indicating to remove or not bad spots (slot
          'BadSpots' in objects of class 'maiges', 'maigesRaw' or
          'maigesANOVA').

 geneGrp: numerical or character specifying the gene group to be
          clustered. This is given by the columns of the slot
          'GeneGrps' in objects of classes 'maiges', 'maigesRaw' and
          'maigesANOVA'.

    path: numerical or character specifying the gene network to be
          clustered. This is given by the items of the slot 'Paths' in
          objects of classes 'maiges', 'maigesRaw' and 'maigesANOVA'.

     ...: additional parameters for 'heatmap' function.

_D_e_t_a_i_l_s:

     This function implements the hierarchical clustering method for
     objects of microarray data defined in this package. The default
     function for hierarchical clustering is the 'hclust'.

_V_a_l_u_e:

     This function display the heatmaps and don't return any object or
     value.

_A_u_t_h_o_r(_s):

     Gustavo H. Esteves <gesteves@vision.ime.usp.br>

_S_e_e _A_l_s_o:

     'somM' and 'kmeansM' for displaying SOM and k-means clusters,
     respectively.

_E_x_a_m_p_l_e_s:

     ## Loading the dataset
     data(gastro)

     ## Doing a hierarchical cluster using all genes, for maigesRaw class
     hierM(gastro.raw, rmGenes=c("BLANK","DAP","LYS","PHE", "Q_GENE","THR","TRP"),
           sLabelID="Sample", gLabelID="Name", doHeat=FALSE)

     ## Doing a hierarchical cluster using all genes, for maigesNorm class
     hierM(gastro.norm, rmGenes=c("BLANK","DAP","LYS","PHE", "Q_GENE","THR","TRP"),
           sLabelID="Sample", gLabelID="Name", doHeat=FALSE)

     ## If you want to show the heatmap do
     hierM(gastro.norm, rmGenes=c("BLANK","DAP","LYS","PHE", "Q_GENE","THR","TRP"),
           sLabelID="Sample", gLabelID="Name", doHeat=TRUE)

     ## If you want to show the hierarchical branch in both margins do
     hierM(gastro.summ, rmGenes=c("BLANK","DAP","LYS","PHE", "Q_GENE","THR","TRP"),
           sLabelID="Sample", gLabelID="Name", doHeat=TRUE, group="B")

     ## If you want to use euclidean distance only into rows (spots or genes)
     hierM(gastro.summ, rmGenes=c("BLANK","DAP","LYS","PHE", "Q_GENE","THR","TRP"),
           sLabelID="Sample", gLabelID="Name", doHeat=FALSE, group="R", distance="euclidean")

