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 red
          and green intensities constituting 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"', '"control"' 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' containing spot quality weights.

    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"' or 'method="control"'.

      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. Method '"none"' computes M-values and
     A-values but does no normalization. Method '"median"' subtracts
     the weighted median from the M-values for each array.

     The loess normalization methods ('"loess"', '"printtiploess"' and
     '"composite"') were proposed by Yang et al (2001, 2002). Smyth and
     Speed (2003) review these methods and describe how the methods are
     implemented in the limma package, including choices of tuning
     parameters. More information on the loess control parameters
     'span' and 'iterations' can be found under 'loessFit'. The default
     values used here are equivalent to those for the older function
     'stat.ma' in the sma package.

     The '"control"' method fits a global loess curve through a set of
     control spots, such as a whole-library titration series, and
     applies that curve to all the other spots.

     The '"robustspline"' method calls 'normalizeRobustSpline'. See
     that function for more documentation.

     '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 unlogged intensities.

     'weights' is normally a matrix giving a quality weight for every
     spot on every array. If 'weights' is instead a vector or a matrix
     with only one column, then the weights will be assumed to be the
     same for every array, i.e., the weights will be probe-specific
     rather than spot-specific.

_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. _Methods_ *31*, 265-273.

_S_e_e _A_l_s_o:

     An overview of limma functions for normalization is given in
     05.Normalization. In particular, see 'normalizeBetweenArrays' for
     between-array normalization.

     The original loess normalization function was the 'statma' funtion
     in the sma package. 'normalizeWithinArrays' is a direct
     generalization of that function, with more options and with
     support for quantitative spot quality weights.

     A different implementation of loess normalization methods is
     provided by the 'maNorm' in the marray package.

     An alternative wrapper for loess normalization, using different
     data classes but calling the limma functions, is provided by the
     'normalise' function in the arrayMagic package.

_E_x_a_m_p_l_e_s:

     #  See normalizeBetweenArrays

