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", bc.method="subtract", offset=0)
     MA.RG(object, bc.method="subtract", offset=0)
     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. For details see 'PrintLayout-class'.

  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"'.

bc.method: character string specifying background correct method, see
          'backgroundCorrect' for options

  offset: numeric value, intensity offset used when computing
          log-ratios, see 'backgroundCorrect'

_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'. Any components found in 'object' will
     preserved except for 'R', 'G', 'Rb', 'Gb' and 'other'.

_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.).
     Methods Volume 31, Issue 4, December 2003, pages 265-273.

_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

