kmeansM              package:maigesPack              R Documentation

_F_u_n_c_t_i_o_n _t_o _d_o _k-_m_e_a_n_s _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 k-means clustering analysis for objects
     of classes 'maiges', 'maigesRaw' and 'maigesANOVA'. Use the
     function 'kmeansMde' for objects of class 'maigesDEcluster'.

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

     kmeansM(data, group=c("C", "R")[1], distance="correlation",
             method="complete", sampleT=NULL, doHier=FALSE, 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' or 'maigesANOVA'.

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

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'

 sampleT: list with 2 vectors. The first one specify the first letter
          of different sample types to be coloured by distinct colours,
          that are given in the second vector. If NULL (default) no
          colour is used.

  doHier: logical indicating if you want to do the hierarchical branch
          in the opposite dimension of clustering. Defaults to FALSE.

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 'Kmeans' function.

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

     This function implements the k-means clustering method for objects
     of microarray data defined in this package. The method uses the
     function 'Kmeans' from package _amap_.

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

     This function display the heatmaps and return invisibly a list
     resulted from the function 'Kmeans'.

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

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

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

     'Kmeans' from package _amap_. 'somM' and 'hierM' for displaying
     SOM and hierarchical clusters, respectively.

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

     ## Loading the dataset
     data(gastro)

     ## Doing a K-means cluster with 2 groups using all genes, for maigesRaw class
     kmeansM(gastro.raw, rmGenes=c("BLANK","DAP","LYS","PHE", "Q_GENE","THR","TRP"),
             sLabelID="Sample", gLabelID="Name", centers=2)

     ## The same as above, but for maigesNorm class
     kmeansM(gastro.norm, rmGenes=c("BLANK","DAP","LYS","PHE", "Q_GENE","THR","TRP"),
             sLabelID="Sample", gLabelID="Name", centers=2)

     ## Another example with 3 groups
     kmeansM(gastro.norm, rmGenes=c("BLANK","DAP","LYS","PHE", "Q_GENE","THR","TRP"),
             sLabelID="Sample", gLabelID="Name", centers=3)

     ## If you want to use euclidean distance to group genes (or spots) with
     ## 4 groups
     kmeansM(gastro.summ, rmGenes=c("BLANK","DAP","LYS","PHE", "Q_GENE","THR","TRP"),
             sLabelID="Sample", gLabelID="Name", centers=4, group="R", distance="euclidean")

