norm2d                 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 _a_n_d _v_a_r_i_a_n_c_e _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. This function performs a
     robust normalization of the variance of the (mean normalized)
     average-across-replicates log ratio differences by scaling the
     (mean normalized) average-across-replicates log ratio difference
     for each gene either by the standard deviation of the log ratio
     differences for that gene across replicates (if bigger than the
     absolute (mean normalized) average-across-replicates log ratio
     difference) or scaling by a constant (a quantile of the
     distribution of standard deviations of (mean normalized)
     average-across-replicates log ratio differences for all genes
     whose standard deviation was bigger than their absolute (mean
     normalized) average-across-replicates log ratio difference.

_U_s_a_g_e:

     norm2d(control.logratio, txt.logratio, control.logintensity, txt.logintensity,
     span = 0.6, quant = 0.99)

_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 intensities.

   quant: Quantile to be used from the distribution of standard
          deviations of log ratio differences across replicates for all
          genes whose standard deviation was smaller than their
          absolute (mean normalized) average-across-replicates log
          ratio difference.

_V_a_l_u_e:

     A vector of mean and variance 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:

     'norm2c','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<-norm2d(lRctl,lRtxt,lIctl,lItxt)

