gal_all                package:CORREP                R Documentation

_G_a_l_a_c_t_o_s_e _M_e_t_a_b_o_l_i_s_m _D_a_t_a

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

     The data is compiled by Mario Medvedovic et al, 2003 based on the
     original full data reported in Ideker et al, 2001. There are a
     total of 205 rows (genes),  20 experiments, and 4 repeated
     measurements in the data. There are 4 classes  (which correspond
     to functional categories). The data contains approximatly 8 of
     missing data. The missing values were filled by applying k-nearest
     neighbor  (k = 12) to impute all the missing values.

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

     data(gal_all)

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

     A data frame with 205 variables on the following 80 replicated
     observations.

     '_w_t_R_G_1' a numeric vector

     '_w_t_R_G_2' a numeric vector

     '_w_t_R_G_3' a numeric vector

     '_w_t_R_G_4' a numeric vector

     '_g_a_l_1_R_G_1' a numeric vector

     '_g_a_l_1_R_G_2' a numeric vector

     '_g_a_l_1_R_G_3' a numeric vector

     '_g_a_l_1_R_G_4' a numeric vector

     '_g_a_l_2_R_G_1' a numeric vector

     '_g_a_l_2_R_G_2' a numeric vector

     '_g_a_l_2_R_G_3' a numeric vector

     '_g_a_l_2_R_G_4' a numeric vector

     '_g_a_l_3_R_G_1' a numeric vector

     '_g_a_l_3_R_G_2' a numeric vector

     '_g_a_l_3_R_G_3' a numeric vector

     '_g_a_l_3_R_G_4' a numeric vector

     '_g_a_l_4_R_G_1' a numeric vector

     '_g_a_l_4_R_G_2' a numeric vector

     '_g_a_l_4_R_G_3' a numeric vector

     '_g_a_l_4_R_G_4' a numeric vector

     '_g_a_l_5_R_G_1' a numeric vector

     '_g_a_l_5_R_G_2' a numeric vector

     '_g_a_l_5_R_G_3' a numeric vector

     '_g_a_l_5_R_G_4' a numeric vector

     '_g_a_l_6_R_G_1' a numeric vector

     '_g_a_l_6_R_G_2' a numeric vector

     '_g_a_l_6_R_G_3' a numeric vector

     '_g_a_l_6_R_G_4' a numeric vector

     '_g_a_l_7_R_G_1' a numeric vector

     '_g_a_l_7_R_G_2' a numeric vector

     '_g_a_l_7_R_G_3' a numeric vector

     '_g_a_l_7_R_G_4' a numeric vector

     '_g_a_l_1_0_R_G_1' a numeric vector

     '_g_a_l_1_0_R_G_2' a numeric vector

     '_g_a_l_1_0_R_G_3' a numeric vector

     '_g_a_l_1_0_R_G_4' a numeric vector

     '_g_a_l_8_0_R_G_1' a numeric vector

     '_g_a_l_8_0_R_G_2' a numeric vector

     '_g_a_l_8_0_R_G_3' a numeric vector

     '_g_a_l_8_0_R_G_4' a numeric vector

     '_w_t_R_1' a numeric vector

     '_w_t_R_2' a numeric vector

     '_w_t_R_3' a numeric vector

     '_w_t_R_4' a numeric vector

     '_g_a_l_1_R_1' a numeric vector

     '_g_a_l_1_R_2' a numeric vector

     '_g_a_l_1_R_3' a numeric vector

     '_g_a_l_1_R_4' a numeric vector

     '_g_a_l_2_R_1' a numeric vector

     '_g_a_l_2_R_2' a numeric vector

     '_g_a_l_2_R_3' a numeric vector

     '_g_a_l_2_R_4' a numeric vector

     '_g_a_l_3_R_1' a numeric vector

     '_g_a_l_3_R_2' a numeric vector

     '_g_a_l_3_R_3' a numeric vector

     '_g_a_l_3_R_4' a numeric vector

     '_g_a_l_4_R_1' a numeric vector

     '_g_a_l_4_R_2' a numeric vector

     '_g_a_l_4_R_3' a numeric vector

     '_g_a_l_4_R_4' a numeric vector

     '_g_a_l_5_R_1' a numeric vector

     '_g_a_l_5_R_2' a numeric vector

     '_g_a_l_5_R_3' a numeric vector

     '_g_a_l_5_R_4' a numeric vector

     '_g_a_l_6_R_1' a numeric vector

     '_g_a_l_6_R_2' a numeric vector

     '_g_a_l_6_R_3' a numeric vector

     '_g_a_l_6_R_4' a numeric vector

     '_g_a_l_7_R_1' a numeric vector

     '_g_a_l_7_R_2' a numeric vector

     '_g_a_l_7_R_3' a numeric vector

     '_g_a_l_7_R_4' a numeric vector

     '_g_a_l_1_0_R_1' a numeric vector

     '_g_a_l_1_0_R_2' a numeric vector

     '_g_a_l_1_0_R_3' a numeric vector

     '_g_a_l_1_0_R_4' a numeric vector

     '_g_a_l_8_0_R_1' a numeric vector

     '_g_a_l_8_0_R_2' a numeric vector

     '_g_a_l_8_0_R_3' a numeric vector

     '_g_a_l_8_0_R_4' a numeric vector

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

     The 205 genes have been classified into four functuional classes
     based on their GO annotations. In the data exmaple provided in the
     vignette, we assume the  four classes as true memberships
     (external knowledge) and use it to evaluate the performances of
     different correlation measured based clustering methods.

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


     http://expression.microslu.washington.edu/expression/kayee/medvedovic2003/medvedovic_bioinf2003.html

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

     Medvedovic M, Yeung KY and Bumgarner RE. 2004. Bayesian Mixture
     Model Based Clustering of Replicated Microarray Data.
     Bioinformatics, 22;20(8):1222-32. Ideker, T., Thorsson, V.,
     Siegel, A. and Hood, L. Testing for Differentially-Expressed Genes
     by Maximum-Likelihood  Analysis of DNA Microarray Data. Journal of
     Computational Biology 7: 805-817 (2000).

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

     data(gal_all)
     ## maybe str(gal_all) ; plot(gal_all) ...

