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)

