norm1a                 package:nudge                 R Documentation

_F_u_n_c_t_i_o_n _f_o_r _n_o_r_m_a_l_i_z_i_n_g _t_h_e _m_e_a_n _o_f _s_i_n_g_l_e _r_e_p_l_i_c_a_t_e _l_o_g _r_a_t_i_o_s

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

     This is essentially the same as the lowess normalization suggested
     in the paper "Statistical methods for identifying differentially
     expressed genes in replicated cDNA microarrays" by Dudoit et al
     (2002), except the loess function is used instead of lowess and
     the recommended span is between 0.6 and 0.8. The normalization is
     done for each gene by subtracting from its log ratio the loess
     estimated mean for the log ratio based on the regression of log
     ratios on log intensities.

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

     norm1a(logratio, logintensity, span = 0.6)

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

logratio: A vector or single-column matrix of log (base 2) ratios of
          gene expressions in two samples.

logintensity: A vector or single-column matrix of log (base 2) total
          intensities (defined as the product) of gene expressions in
          the two samples.

    span: Proportion of data used to fit the loess regression of the
          log ratios on the log intensities.

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

     A vector or single-column matrix of mean normalized log ratios.

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

     N. Dean and A. E. Raftery

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

     N. Dean and A. E. Raftery (2005). Normal uniform mixture
     differential gene expression detection for cDNA microarrays. BMC
     Bioinformatics. 6, 173-186.

     <URL: http://www.biomedcentral.com/1471-2105/6/173>

     S. Dudoit, Y. H. Yang, M. Callow and T. Speed (2002). Statistical
     methods for identifying differentially expressed genes in
     replicated cDNA microarray experiments. Stat. Sin. 12, 111-139.

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

     'norm1b','norm1c','norm1d','norm2c','norm2d'

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

     data(like)
     lR<-log(like[,1],2)-log(like[,2],2)
     lI<-log(like[,1],2)+log(like[,2],2)

     lRnorm<-norm1a(lR,lI)

