BssWssFast           package:iterativeBMA           R Documentation

_B_e_t_w_e_e_n-_g_r_o_u_p_s _s_u_m-_o_f-_s_q_u_a_r_e_s _t_o _w_i_t_h_i_n-_g_r_o_u_p_s _s_u_m-_o_f-_s_q_u_a_r_e_s
_r_a_t_i_o

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

     This is a univariate technique to select relevant genes in
     classification of microarray data. In classifying samples of
     microarray data, this ratio is computed for each gene.  A large
     between-groups to within-groups sum-of-squares  ratio indicates a
     potentially relevant gene.

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

     BssWssFast (X, givenClassArr, numClass = 2)

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

       X: data matrix where columns are variables and rows are
          observations. In the case of gene expression data, the
          columns (variables) represent genes, while the rows
          (observations) represent samples or experiments.

givenClassArr: class vector for the observations (samples or 
          experiments). Class numbers are assumed to start from 0, and
          the length of this class vector should be equal to the number
          of rows in X. In the case of 2-class data, we expect the
          class vector consists of zero's and one's.

numClass: number of classes. The default is 2.

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

     This function is called by 'iterateBMAglm.2class'.

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

     A list of 2 elements are returned: 

       x: A vector containing the BSS/WSS ratios in descending order.

      ix: A vector containing the indices corresponding to the sorted
          ratios.

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

     Dudoit, S., Fridlyand, J. and Speed, T.P. (2002) Comparison of
     discrimination methods for the classification of tumors using gene
     expression data. Journal of the American Statistical Association
     97: 77-87.

     Yeung, K.Y., Bumgarner, R.E. and Raftery, A.E. (2005)  Bayesian
     Model Averaging: Development of an improved multi-class, gene
     selection and classification tool for microarray data. 
     Bioinformatics 21: 2394-2402.

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

     'iterateBMAglm.train',  'trainData', 'trainClass'

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

     data(trainData)
     data(trainClass)

     ret.bsswss <- BssWssFast (X=t(exprs(trainData)), givenClassArr=trainClass, numClass = 2)

