justvsn                 package:vsn                 R Documentation

_N_o_r_m_a_l_i_z_a_t_i_o_n _w_i_t_h _v_s_n

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

     'justvsn' fits the vsn model to data and returns an
     'ExpressionSet'. It is a simple wrapper for 'vsn2' and 'predict'.

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

     justvsn(x, reference, strata, ...)

     ## S4 method for signature 'RGList':
     justvsn(x, reference, strata, backgroundsubtract=FALSE, ...)

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

       x: An object containing the data to which the model is to be
          fitted. Methods exist for 'ExpressionSet', 'AffyBatch' (from
          the 'affy' package) and 'RGList' (from the 'limma' package).

reference, strata, ...: As in 'vsn2'.

backgroundsubtract: Logical of length 1, it applies only to the method
          for 'RGList' objects and indicates whether local background
          estimates in the 'Rb' and 'Gb' slots of 'x' should be
          subtracted before fitting vsn.

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

     See 'vsn2'.

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

     An 'ExpressionSet'.

     If 'x' is an 'ExpressionSet', then the dimensions and the metadata
     of the returned object will be the same, and its matrix of
     expression values are the normalized and transformed version of
     the input.

     If 'x' is an 'AffyBatch', probe-wise background correction and
     between-array normalization are done by 'vsn2' on the perfect
     match (PM) values only. Probeset summaries are calculated with the
     medianpolish algorithm of 'rma'.

     If 'x' is an 'RGList', then 'vsn2' is applied to the matrix
     'cbind(x$R, x$G)'. If 'backgroundsubtract=TRUE', then 'cbind(x$Rb,
     x$Gb)' is subtracted before.

_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 AffyBatch
     ##--------------------------------------------------
     data("affybatch.example")
     data("cdfenv.example")
     eabe = justvsn(affybatch.example)

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

