4.Normalization            package:limma            R Documentation

_N_o_r_m_a_l_i_z_a_t_i_o_n _o_f _M_i_c_r_o_a_r_r_a_y _D_a_t_a

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

     This page gives an overview of the LIMMA functions available to
     normalize data from spotted two-colour microarrays. Smyth and
     Speed (2003) give an overview of the normalization techniques
     implemented in the functions.

     Usually data from spotted microarrays will be normalized using
     'normalizeWithinArrays'. A minority of data will also be
     normalized using 'normalizeBetweenArrays' if diagnostic plots
     suggest a difference in scale between the arrays.

     In rare circumstances, data might be normalized using
     'normalizeForPrintorder' before using 'normalizeWithinArrays'.

     All the normalization routines take account of spot quality
     weights which might be set in the data objects. The weights can be
     temporarily modified using 'modifyWeights' to, for example, remove
     ratio control spots from the normalization process.

     If one is planning analysis of single-channel information from the
     microarrays rather than analysis of differential expression based
     on log-ratios, then the data should be normalized using a single
     channel-normalization technique. Single channel normalization uses
     further options of the 'normalizeBetweenArrays' function. For more
     details see the _LIMMA User's Guide_ which includes a section on
     single-channel normalization.

     'normalizeWithinArrays' uses utility functions 'MA.RG', 'loessFit'
     and 'normalizeRobustSpline'. 'normalizeBetweenArrays' uses utility
     functions 'normalizeMedians', 'normalizeMedianDeviations' and
     'normalizeQuantiles', none of which need to be called directly by
     users.

_B_a_c_k_g_o_u_n_d _C_o_r_r_e_c_t_i_o_n:

     Usually one doesn't need to explicitly ask for background
     correction of the intensities because this is done by default by
     'normalizeWithinArrays', which subtracts the background from the
     foreground intensities before applying the normalization method.
     This default background correction method can be over-ridden by
     using 'backgroundCorrect' which offers a number of alternative
     background correct methods to simple subtraction. Simply use
     'backgroundCorrect' to correct the 'RGList' before applying
     'normalizeWithinArrays'.

     'backgroundCorrect' uses utility functions 'ma3x3.matrix',
     'ma3x3.spottedarray', 'fit.normexp', 'signal.normexp' and
     'm2loglik.normexp'.

     'kooperberg' is a Bayesian background correction tool designed
     specifically for GenePix data. 'kooperberg' is not currently used
     as the default method for GenePix data because it is
     computationally intensive. It requires several columns of the
     GenePix data files which are not read in by read.maimages, so you
     will need to use 'read.series' instead of 'read.maimages' if you
     wish to use 'kooperberg'.

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

     Gordon Smyth

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

     Smyth, G. K., and Speed, T. P. (2003). Normalization of cDNA
     microarray data. In: _METHODS: Selecting Candidate Genes from DNA
     Array Screens: Application to Neuroscience_, D. Carter (ed.).
     Methods Volume 31, Issue 4, December 2003, pages 265-273. <URL:
     http://www.statsci.org/smyth/pubs/normalize.pdf>

