array2ade4               package:made4               R Documentation

_C_o_n_v_e_r_t_s _m_i_c_r_o_a_r_r_a_y _i_n_p_u_t _d_a_t_a _i_n_t_o _a _d_a_t_a _f_r_a_m_e _s_u_i_t_a_b_l_e _f_o_r _a_n_a_l_y_s_i_s
_i_n _A_D_E_4.

_D_e_s_c_r_i_p_t_i_o_n:

     Converts input data into a data.frame suitable for analysis in
     ADE4. This function is called by 'bga' and other made4 function.

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

     array2ade4(dataset, pos = FALSE, trans = TRUE)

_A_r_g_u_m_e_n_t_s:

 dataset: A 'matrix', 'data.frame',  'exprSet' or 'marrayRaw'.   If the
          input is gene expression data in a 'matrix' or 'data.frame'.
          The  rows and columns are expected to contain the variables
          (genes) and cases (array samples)  respectively. 

     pos: Logical indicating whether to add an integer to 'dataset', to
          generate positive 'data.frame'.  Required  for  'dudi.coa' or
          'dudi.nsc'

   trans: Logical indicating whether 'dataset' should be transposed.
          Both 'between'  group analysis, and 'coinertia' analysis
          expect that the cases (array samples)  are contained in the
          rows and that the variables (genes) are in the columns. Thus
          the 'data.frame' must  be transposed prior to analysis

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

     'bga' and other functions in made4 call this function and it is
     generally *not* necessary to call 'array2ade4' this directly.

     array2ade4 calls 'getdata', and will accept a 'matrix',
     'data.frame',  'exprSet' or 'marrayRaw' format. It  will also
     transpose data or add a integer to generate a positive data
     matrix. 

     If the input data contains missing values (NA), these must first
     be removed or imputed (see the R libraries impute() or pamr()).

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

     Returns a data.frame suitable for analysis by ade4 or made4
     functions.

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

     Aedin Culhane

_R_e_f_e_r_e_n_c_e_s:

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

     'getdata', 'getIntensityMatrix'

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

     library(affy)
     data(geneData)
     class(geneData)
     dim(geneData)
     dim(array2ade4(geneData))
     class(array2ade4(geneData))

     eset <- new("exprSet", exprs=geneData)
     eset
     class(eset)
     class(array2ade4(eset))
     dim(array2ade4(eset))

