hierMde              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 classe 'maigesDEcluster'.

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

     hierMde(data, group=c("C", "R", "B")[1], distance="correlation",
             method="complete", doHeat=TRUE, sLabelID="SAMPLE",
             gLabelID="GeneName", idxTest=1, adjP="BH",
             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',
          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.

 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 'heatmap' function.

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

     This function implements the hierarchical clustering method for
     objects resulted from differential expression analysis. The
     default function for hierarchical clustering is the 'hclust'. 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 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 bootstrap from t statistic test fot 'Type' sample label, k=1000
     ## specifies one thousand bootstraps
     gastro.ttest = deGenes2by2Ttest(gastro.summ, sLabelID="Type")

     ## Hierarchical cluster adjusting p-values by FDR, and showing all genes
     ## with p-value < 0.05
     hierMde(gastro.ttest, sLabelID="Type", adjP="BH", nDEgenes=0.05)

