trainData          package:iterativeBMAsurv          R Documentation

_S_a_m_p_l_e _T_r_a_i_n_i_n_g _D_a_t_a _f_o_r _t_h_e _I_t_e_r_a_t_i_v_e _B_M_A _A_l_g_o_r_i_t_h_m _f_o_r _S_u_r_v_i_v_a_l _A_n_a_l_y_s_i_s

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

     This is an adapted diffuse large B-cell lymphoma (DLBCL) dataset
     from  Rosenwald et al. (2002). This data matrix consists of the
     expression  levels from 65 DLBCL samples (rows), and 100 top
     univariate genes  (columns). This dataset is used as a sample
     training set in our examples.

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

     data(trainData)

_F_o_r_m_a_t:

     The data matrix is called 'trainData'. Each entry in the matrix
     represents the expression level of one gene from  a DLBCL sample.

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

     We started with the full expression data from Rosenwald et al.
     (2002), which is available along with corresponding patient
     information at their  supplemental website <URL:
     http://llmpp.nih.gov/DLBCL/>. We selected a subset of the 160
     training samples, and then performed Cox Proportional  Hazards
     Regression to obtain the 100 genes with the highest log
     likelihood.  The filtered dataset consists of 65 training samples,
     and it is available at our website <URL:
     http://expression.washington.edu/ibmasurv/protected>.

_S_o_u_r_c_e:

     Full dataset: <URL: http://llmpp.nih.gov/DLBCL/>.

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

     Rosenwald, A., Wright, G., Wing, C., Connors, J., Campo, E. et al.
     (2002).  The Use of Molecular Profiling to Predict Survival After
     Chemotherapy for Diffuse Large-B-Cell Lymphoma.  The New England
     Journal of Medicine, 346(25), 1937-1947.

