kmeansMde             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 class 'maigesDEcluster'.

_U_s_a_g_e:

     kmeansMde(data, group=c("C", "R")[1], distance="correlation",
               method="complete", sampleT=NULL, doHier=FALSE, sLabelID="SAMPLE",
               gLabelID="GeneName", idxTest=1, adjP="none", nDEgenes=0.05, ...)

_A_r_g_u_m_e_n_t_s:

    data: object of class 'maigesDEcluster'.

   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.

 idxTest: numerical index of the test to be used to sort the genes when
          clustering objects of class 'maigesDEcluster'.

    adjP: string specifying the method of p-value adjustment. May be
          'none', 'Bonferroni', 'Holm', 'Hochberg', 'SidakSS',
          'SidakSD', 'BH', 'BY'.

nDEgenes: number of DE genes to be selected. If a real number in (0,1)
          all genes with p.value <= 'nDEgenes' will be used. If an
          integer, the 'nDEgenes' genes with smaller p-values will be
          used.

     ...: additional parameters for 'Kmeans' function.

_D_e_t_a_i_l_s:

     This function implements the k-means clustering method for objects
     resulted from differential analysis. The method uses the function
     'Kmeans' from package _amap_. For the adjustment of p-values in
     the selection of genes differentially expressed, we use the
     function 'mt.rawp2adjp' from package _multtest_.

_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_. 'mt.rawp2adjp' from package
     _multtest_. 'somM' and 'hierM' for displaying SOM and hierarchical
     clusters, respectively.

_E_x_a_m_p_l_e_s:

     ## Loading the dataset
     data(gastro)

     ## Doing bootstrap from t statistic test fot 'Type' sample label, k=1000
     ## specifies one thousand bootstraps
     gastro.ttest = deGenes2by2Ttest(gastro.summ, sLabelID="Type")

     ## K-means cluster with 2 groups adjusting p-values by FDR, and showing all genes
     ## with p-value < 0.05
     kmeansMde(gastro.ttest, sLabelID="Type", adjP="BH", nDEgenes=0.05, centers=2)

     ## K-means cluster with 3 groups adjusting p-values by FDR, and showing all genes
     ## with p-value < 0.05
     kmeansMde(gastro.ttest, sLabelID="Type", adjP="BH", nDEgenes=0.05, centers=3)

     dev.off()

