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' 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'.

_V_a_l_u_e:

     '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.

     'vsnrma' returns an 'ExpressionSet'.

_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)

