justvsn                 package:vsn                 R Documentation

_W_r_a_p_p_e_r _f_u_n_c_t_i_o_n_s _f_o_r _v_s_n

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

     'justvsn' returns the vsn-normalised intensities in an object
     generally of the same class as its first argument (see the man
     page of 'predict' for details). It preserves the metadata. It is
     equivalent to calling


       fit = vsn2(x, ...)
       nx = predict(fit, newdata=x, useDataInFit = TRUE)

     'vsnrma' is a wrapper around 'vsn2' and 'rma'

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

     justvsn(x, ...)
     vsnrma(x, ...)

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

       x: For 'justvsn', any kind of object for which 'vsn2' methods
          exist. For 'vsnrma', an 'AffyBatch'.

     ...: Further arguments that get passed on to 'vsn2'.

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

     'vsnrma' does probe-wise background correction and between-array
     normalization by calling 'vsn2' on the perfect match (PM) values
     only. Probeset summaries are calculated with the medianpolish
     algorithm of 'rma'.

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

     Wolfgang Huber <URL: http://www.ebi.ac.uk/huber>

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

     'vsn2'

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

     ##--------------------------------------------------
     ## use "vsn2" to produce a "vsn" object
     ##--------------------------------------------------
     data("kidney")
     fit = vsn2(kidney)
     nkid = predict(fit, newdata=kidney)

     ##--------------------------------------------------
     ## justvsn on ExpressionSet
     ##--------------------------------------------------
     nkid2 = justvsn(kidney)
     stopifnot(identical(exprs(nkid), exprs(nkid2)))

     ##--------------------------------------------------
     ## justvsn on RGList
     ##--------------------------------------------------
     rg = new("RGList", list(R=exprs(kidney)[,1,drop=FALSE], G=exprs(kidney)[,2,drop=FALSE]))
     erge = justvsn(rg)

