norm1c                 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 _a_v_e_r_a_g_e-_a_c_r_o_s_s-_r_e_p_l_i_c_a_t_e_s _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 and average-across-replicates
     log ratios are used and the recommended span is between 0.6 and
     0.8. The normalization is done for each gene by subtracting from
     its average-across-replicates log ratio the loess estimated mean
     for average-across-replicates log ratio based on the loess
     regression of average-across-replicates log ratios on
     average-across-replicates log total intensities.

_U_s_a_g_e:

     norm1c(logratio, logintensity, span = 0.6)

_A_r_g_u_m_e_n_t_s:

logratio: A multiple-column matrix of replicates of log (base 2) ratios
          of gene expressions in two samples.

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

    span: Proportion of data used to fit the loess regression of the
          average-across-replicates log ratios on the
          average-across-replicates log total intensities

_V_a_l_u_e:

     A vector of mean normalized average-across-replicates 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:

     'norm1d','norm1a','norm1b','norm2c','norm2d'

_E_x_a_m_p_l_e_s:

     data(hiv)
     lR<-log(hiv[,1:4],2)-log(hiv[,5:8],2)
     lI<-log(hiv[,1:4],2)+log(hiv[,5:8],2)

     lRnorm<-norm1c(lR,lI)

