norm2c                 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 _d_i_f_f_e_r_e_n_c_e_s

_D_e_s_c_r_i_p_t_i_o_n:

     This normalization is used when the two samples (control and
     treatment, say) are not being directly compared on the slides but
     instead are being compared to a common reference sample. The
     quantity of interest for each gene is thus the average difference
     between control and treatment log ratios. The normalization is
     done for each gene by subtracting from its
     average-across-replicates log ratio difference the loess estimated
     mean for average-across-replicates log ratio difference based on
     the loess regression of average-across-replicates log ratio
     differences on average-across-replicates log total intensities.

_U_s_a_g_e:

     norm2c(control.logratio, txt.logratio, control.logintensity, txt.logintensity,
     span = 0.6)

_A_r_g_u_m_e_n_t_s:

control.logratio: A multiple-column matrix of replicates of log (base
          2) ratios of gene expressions for the control versus
          reference slides.

txt.logratio: A multiple-column matrix of replicates of log (base 2)
          ratios of gene expressions for the treatment versus reference
          slides.

control.logintensity: A multiple-column matrix of replicates of log
          (base 2) total intensities (defined as the product) of gene
          expressions for the control versus reference slides.

txt.logintensity: A multiple-column matrix of replicates of log (base
          2) total intensities (defined as the product) of gene
          expressions for the treatment versus reference slides.

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

_V_a_l_u_e:

     A vector of mean normalized average-across-replicates log ratio
     differences.

_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:

     'norm2d','norm1a','norm1b','norm1c','norm1d'

_E_x_a_m_p_l_e_s:

     apo<-read.csv("http://www.stat.berkeley.edu/users/terry/zarray/Data/ApoA1/rg_a1ko_morph.txt",
     header=TRUE)
     rownames(apo)<-apo[,1]
     apo<-apo[,-1]
     apo<-apo+1

     lRctl<-log(apo[,c(seq(2,16,2))],2)-log(apo[,c(seq(1,15,2))],2)
     lRtxt<-log(apo[,c(seq(18,32,2))],2)-log(apo[,c(seq(17,31,2))],2)
     lIctl<-log(apo[,c(seq(2,16,2))],2)+log(apo[,c(seq(1,15,2))],2)
     lItxt<-log(apo[,c(seq(18,32,2))],2)+log(apo[,c(seq(17,31,2))],2)

     lRnorm<-norm2c(lRctl,lRtxt,lIctl,lItxt)

