normalize.AffyBatch.vsn         package:vsn         R Documentation

_W_r_a_p_p_e_r _f_o_r _v_s_n _t_o _b_e _u_s_e_d _a_s _a _n_o_r_m_a_l_i_z_a_t_i_o_n _m_e_t_h_o_d _w_i_t_h _e_x_p_r_e_s_s_o

_D_e_s_c_r_i_p_t_i_o_n:

     Wrapper for 'vsn2' to be used as a normalization method with the
     expresso function of the package affy. The expresso function is
     deprecated, consider using 'justvsn' instead. The
     normalize.AffyBatch.vsn can still be useful on its own, as it
     provides some additional control of the normalization process
     (fitting on subsets, alternate transform parameters).

_U_s_a_g_e:

       normalize.AffyBatch.vsn(
          abatch,
          reference,
          strata = NULL,
          subsample = if (nrow(exprs(abatch))>30000L) 30000L else 0L,
          subset,
          log2scale = TRUE,
          log2asymp=FALSE,
          ...)

_A_r_g_u_m_e_n_t_s:

  abatch: An object of type 'AffyBatch'.

reference: Optional, a 'vsn' object from a previous fit. If this
          argument is specified, the data in 'x' are normalized
          "towards" an existing set of reference arrays whose
          parameters are stored in the object 'reference'. If this
          argument is not specified, then the data in 'x' are
          normalized "among themselves". See  'vsn2' for details.

  strata: The 'strata' functionality is not supported, the parameter is
          ignored.

subsample: Is passed on to 'vsn2'.

  subset: This allows the specification of a subset of expression
          measurements to be used for the vsn fit. The transformation
          with the parameters of this fit is then, however, applied to
          the whole dataset. This is useful for excluding expression
          measurements that are known to be differentially expressed or
          control probes that may not match the vsn model, thus
          avoiding that they influence the normalization process. This
          operates at the level of probesets, not probes. Both 'subset'
          and 'subsample' can be used together.

log2scale: If TRUE, this will perform a global affine transform on the
          data to put them on a similar scale as the original
          non-transformed data. Many users prefer this.  Fold-change
          estimates are not affected by this transform. In some
          situations, however, it may be helpful to turn this off,
          e.g., when comparing independently normalized subsets of the
          data.

log2asymp: If TRUE, this will perform a global affine transform on the
          data to make the generalized log (asinh) transform be
          asymptotically identical to a log base 2 transform. Some
          people find this helpful. Only *one* of 'log2scale' or
          'log2asymp' can be set to TRUE. Fold-change estimates are not
          affected by this transform.

     ...: Further parameters for 'vsn2'.

_D_e_t_a_i_l_s:

     Please refer to the _Details_ and _References_ sections of the man
     page for 'vsn2' for more details about this method.

     *Important note*: after calling 'vsn2', the function
     'normalize.AffyBatch.vsn' *exponentiates* the data (base 2). This
     is done in order to make the behavior of this function similar to
     the other normalization methods in affy. That packages uses the
     convention of taking the logarithm to base in subsequent analysis
     steps (e.g. in 'medpolish').

_V_a_l_u_e:

     An object of class 'AffyBatch'. The 'vsn' object returned, which
     can be used as 'reference' for subsequent fits, is provided by
     'description(abatch)@preprocessing$vsnReference'.

_A_u_t_h_o_r(_s):

     D. P. Kreil <URL: http://bioinf.boku.ac.at/>, 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:

     ## Please see vignette.

