normalizeWithinArrays         package:limma         R Documentation

_N_o_r_m_a_l_i_z_e _W_i_t_h_i_n _A_r_r_a_y_s

_D_e_s_c_r_i_p_t_i_o_n:

     Normalize the expression log-ratios for one or more two-colour
     spotted microarray experiments so that the log-ratios average to
     zero within each array or sub-array.

_U_s_a_g_e:

     normalizeWithinArrays(object, layout, method="printtiploess", weights=object$weights, span=0.3, iterations=4, controlspots=NULL, df=5, robust="M")
     MA.RG(object, log.transform=TRUE)
     RG.MA(object)

_A_r_g_u_m_e_n_t_s:

  object: object of class 'list', 'RGList' or 'MAList' containing
          two-color microarray data

  layout: list specifying the dimensions of the spot matrix and the
          grid matrix

  method: character string specifying the normalization method. Choices
          are '"none"', '"median"', '"loess"', '"printtiploess"',
          '"composite"' and '"robustspline"'. A partial string
          sufficient to uniquely identify the choice is permitted.

 weights: numeric matrix or vector of the same size and shape as the
          components of 'object'. Will use by default weights found in
          'object' if they exist.

    span: numeric scalar giving the smoothing parameter for the 'loess'
          fit

iterations: number of iterations used in loess fitting.  More
          iterations give a more robust fit.

controlspots: numeric or logical vector specifying the subset of spots
          which are non-differentially expressed control spots, for use
          with 'method="composite"'

      df: degrees of freedom for spline if 'method="robustspline"'

  robust: robust regression method if 'method="robustspline"'.  Choices
          are '"M"' or '"MM"'.

log.transform: logical indicating whether intensities should be log2
          transformed. If 'FALSE' then intensities are assumed to be
          already logged.

_D_e_t_a_i_l_s:

     Normalization is intended to remove from the expression measures
     any systematic trends which arise from the microarray technology
     rather than from differences between the probes or between the
     target RNA samples hybridized to the arrays.

     This function normalizes M-values (log-ratios) for dye-bias within
     each array. Apart from 'method="none"' and 'method="median"', all
     the normalization methods make use of the relationship between
     dye-bias and intensity. The loess normalization methods were
     proposed by Yang et al (2001, 2002). Smyth and Speed (2003) give a
     detailed statement of the methods.

     More information on the loess control parameters 'span' and
     'iterations' can be found under 'loessFit'. The default values
     given here are equivalent to those for the older function
     'stat.ma' in the SMA package.

     The '"robustspline"' method calls 'normalizeRobustSpline'.

     'MA.RG' converts an unlogged 'RGList' object into an 'MAList'
     object. 'MA.RG(object)' is equivalent to
     'normalizeWithinArrays(object,method="none")'.

     'RG.MA(object)' converts back from an 'MAList' object to a
     'RGList' object with intensities on the log2 scale.

_V_a_l_u_e:

     An object of class 'MAList'.

_A_u_t_h_o_r(_s):

     Gordon Smyth

_R_e_f_e_r_e_n_c_e_s:

     Yang, Y. H., Dudoit, S., Luu, P., and Speed, T. P. (2001).
     Normalization for cDNA microarray data. In _Microarrays: Optical
     Technologies and Informatics_, M. L. Bittner, Y. Chen, A. N.
     Dorsel, and E. R. Dougherty (eds), Proceedings of SPIE, Vol. 4266,
     pp. 141-152. 

     Yang, Y. H., Dudoit, S., Luu, P., Lin, D. M., Peng, V., Ngai, J.,
     and Speed, T. P. (2002). Normalization for cDNA microarray data: a
     robust composite method addressing single and multiple slide
     systematic variation. _Nucleic Acids Research_ *30*(4):e15.

     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.). To
     appear.

_S_e_e _A_l_s_o:

     An overview of LIMMA functions for normalization is given in
     4.Normalization.

     See also 'normalizeBetweenArrays' and 'maNorm' in the marrayNorm
     package.

_E_x_a_m_p_l_e_s:

     #  See normalizeBetweenArrays

