xsScores                 package:XDE                 R Documentation

_A_l_t_e_r_n_a_t_i_v_e _c_r_o_s_s-_s_t_u_d_y _s_c_o_r_e_s _o_f _d_i_f_f_e_r_e_n_t_i_a_l _e_x_p_r_e_s_s_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     Alternative cross-study scores of differential expression

_U_s_a_g_e:

     xsScores(statistic, N)

_A_r_g_u_m_e_n_t_s:

statistic: a matrix of study-specific estimates of effect size. Rows
          are genes and columns are studies.

       N: numerical vector: the number of samples in each study (the
          length should be the number of columns in 'statistic')

_V_a_l_u_e:

     numeric

_A_u_t_h_o_r(_s):

     R. Scharpf

_R_e_f_e_r_e_n_c_e_s:

     J.K. Choi, U. Yu, S. Kim, and O.J. Yoo (2003), Combining multiple
     microarray studies and modeling interstudy variation,
     Bioinformatics, 19(1) I84-I90.  

     E. Garrett-Mayer, G. Parmigiani, X. Zhong, L. Cope, and E.
     Gabrielson (2007), Cross-study validation and combined analysis of
     gene expression microarray data, Biostatistics, September

     R. Scharpf et al., A Bayesian Model for Cross-Study Differential
     Gene Expression, Technical Report 158, Johns Hopkins University,
     Department of Biostatistics, 2007

_S_e_e _A_l_s_o:

     the GeneMeta package, 'ssStatistic'

_E_x_a_m_p_l_e_s:

       data(expressionSetList)
       t <- ssStatistic(statistic="t", phenotypeLabel="adenoVsquamous", esetList=expressionSetList)
       tScores <- xsScores(t, N=nSamples(expressionSetList))

