maNorm                package:marray                R Documentation

_S_i_m_p_l_e _l_o_c_a_t_i_o_n _a_n_d _s_c_a_l_e _n_o_r_m_a_l_i_z_a_t_i_o_n _f_u_n_c_t_i_o_n

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

     This function is a simple wrapper function around the main
     normalization function 'maNormMain'. It allows the user to choose
     from a set of six basic location and scale normalization
     procedures. The function operates on an object of class
     '"marrayRaw"' (or possibly '"marrayNorm"', if normalization is
     performed in several steps) and returns an object of class
     '"marrayNorm"'.

_U_s_a_g_e:

     maNorm(mbatch, norm=c("printTipLoess", "none", "median", "loess",
     "twoD", "scalePrintTipMAD"), subset=TRUE, span=0.4, Mloc=TRUE,
     Mscale=TRUE, echo=FALSE, ...)

_A_r_g_u_m_e_n_t_s:

  mbatch: Object of class 'marrayRaw', containing intensity  data for
          the batch of arrays to be normalized.  An object of class
          '"marrayNorm"' may also be passed if  normalization is
          performed in several steps.

    norm: Character string specifying the normalization procedures: 

          _n_o_n_e no normalization

          _m_e_d_i_a_n for global median location normalization

          _l_o_e_s_s for global intensity or A-dependent location
               normalization using  the 'loess' function

          _t_w_o_D for 2D spatial location normalization using the  'loess'
               function

          _p_r_i_n_t_T_i_p_L_o_e_s_s for within-print-tip-group intensity dependent
               location  normalization using the 'loess' function

          _s_c_a_l_e_P_r_i_n_t_T_i_p_M_A_D for within-print-tip-group intensity
               dependent  location normalization followed by
               within-print-tip-group scale normalization  using the
               median absolute deviation (MAD). 

               This argument can be specified using the first letter of
               each method.

  subset: A "logical" or "numeric" vector indicating the subset of
          points used to compute the  normalization values.

    span: The argument 'span' which controls the degree of smoothing in
          the 'loess' function.

    Mloc: If 'TRUE', the location normalization values are stored in
          the slot 'maMloc' of the object of class '"marrayNorm"'
          returned by the function, if 'FALSE', these values are not
          retained.

  Mscale: If 'TRUE', the scale normalization values are stored in the
          slot 'maMscale' of the object of class '"marrayNorm"'
          returned by the function, if 'FALSE', these values are not
          retained.

    echo: If 'TRUE', the index of the array currently being normalized
          is printed.

     ...: Misc arguments

_D_e_t_a_i_l_s:

     See 'maNormMain' for details and also more general procedures.

_V_a_l_u_e:

   mnorm: An object of class '"marrayNorm"', containing the normalized
          intensity data.

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

     Sandrine Dudoit, <URL: http://www.stat.berkeley.edu/~sandrine>.

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

     S. Dudoit and Y. H. Yang. (2002). Bioconductor R packages for
     exploratory analysis and normalization of cDNA microarray data. In
     G. Parmigiani, E. S. Garrett, R. A. Irizarry and S. L. Zeger,
     editors, _The Analysis of Gene Expression Data: Methods and
     Software_, Springer, New York.


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


     Y. H. Yang, S. Dudoit, P. Luu, D. M. Lin, V. Peng, J. Ngai, and T.
     P. Speed (2002). Normalization for cDNA microarray data: a robust
     composite method addressing single and multiple slide systematic
     variation. _Nucleic Acids Research_, Vol. 30, No. 4.

_S_e_e _A_l_s_o:

     'maNormMain', 'maNormScale'.

_E_x_a_m_p_l_e_s:

     # Examples use swirl dataset, for description type ? swirl
     data(swirl)

     # Global median normalization for swirl arrays 2 and 3
     mnorm<-maNorm(swirl[,2:3], norm="median", echo=TRUE)

     # Within-print-tip-group loess location normalization for swirl array 1
     mnorm<-maNorm(swirl[,1], norm="p", span=0.45)

