norm1b                 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_r _j_u_s_t _t_h_e _v_a_r_i_a_n_c_e) _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 performs a robust loess normalization of the variance of the
     log ratios in a single replicate experiment by regressing the
     absolute (mean normalized) log ratios on the log intensities and
     using the fitted values to scale the (mean normalized) log ratio
     for each gene.

_U_s_a_g_e:

     norm1b(logratio, logintensity, span1 = 0.6, span2 = 0.2, mean.norm=TRUE)

_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, if mean.norm is FALSE the
          log ratios should be already mean normalized.

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

   span1: Proportion of data used to fit the loess regression of the
          log ratios on the log intensities for the mean normalization.

   span2: Proportion of data used to fit the loess regression of the
          absolute (mean normalized) log ratios on the log intensities
          for the variance normalization.

mean.norm: A logical value indicating whether or not a mean
          normalization should be performed prior to the variance
          normalization.

_V_a_l_u_e:

     A vector or single-column matrix of mean and variance normalized
     log (base 2) ratios of gene expressions in two samples.

_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_e_e _A_l_s_o:

     'norm1a','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<-norm1b(lR,lI,mean.norm=TRUE)

