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)

