normaliseIllumina         package:beadarray         R Documentation

_N_o_r_m_a_l_i_s_e _I_l_l_u_m_i_n_a _e_x_p_r_e_s_s_i_o_n _d_a_t_a

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

     Normalises expression intensities from an 'ExpressionSetIllumina' 
     object so that the intensities are comparable between arrays.

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

     normaliseIllumina(BSData, method="quantile", transform="none", T=NULL, ...)

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

  BSData: an 'ExpressionSetIllumina' object

  method: character string specifying normalisation method (options are
          '"quantile"', '"qspline"', '"vsn"', '"rankInvariant"',
          '"median"' and '"none"'.

transform: character string specifying transformation to apply to the
          data prior to normalisation (options are '"none"',  '"log2"'
          and '"vst"'

       T: A target distribution vector used when
          'method="rankInvariant"' normalisation.  If 'NULL', the mean
          is used.

     ...: further arguments to be passed to 'lumiT'

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

     Normalisation 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.

     In this function, the 'transform' specified by the user is 
     applied prior to the chosen normalisation procedure.

     When 'transform="vst"' the variance-stabilising transformation
     from the 'lumi' package is applied to the data. Refer to the
     'lumiT' documentation for further particulars.  Note that  the
     Detection P values are only passed on when they are available 
     (i.e. not NA) 

     For further particulars on the different normalisation methods
     options refer to the individual help pages ('?normalize.quantiles'
     for '"quantile"', '?normalize.qspline' for "qspline",
     '?rankInvariantNormalise' for '"rankInvariant"',
     '?medianNormalise' for '"median"' and '?vsn2' for '"vsn"'.

     For median normalisation, the intensity for each gene is adjusted
     by subtracting the median of all genes on the array and then
     adding the median across all arrays. The effect is that each array
     then has the same median value.

     Note: If your 'BSData' object contains data already on the
     log-scale, be careful that you choose an appropriate 'transform'
     to avoid transforming it twice.  The same applies for the '"vst"'
     transformation and '"vsn"' normalisation methods which require the
     expression data stored in 'BSData' to be on the original
     (un-logged) scale.  When 'method="vsn"', transform must be set to 
     '"none"', since this method transforms and normalises the data as 
     part of the model.

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

     An 'ExpressionSetIllumina' object which conatains the transformed
     and normalised expression values for each array.

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

     Matt Ritchie

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

     data(BSData)
     BSData.norm = normaliseIllumina(BSData, method="quantile", transform="log2")

