vsn                   package:vsn                   R Documentation

_V_a_r_i_a_n_c_e _s_t_a_b_i_l_i_z_a_t_i_o_n _a_n_d _c_a_l_i_b_r_a_t_i_o_n _f_o_r _m_i_c_r_o_a_r_r_a_y _d_a_t_a.

_D_e_s_c_r_i_p_t_i_o_n:

     Robust estimation of variance-stabilizing and calibrating 
     transformations for microarray data. This is the main function of
     this package; see also the vignette vsn.pdf.

_U_s_a_g_e:

     vsn(intensities,
         lts.quantile = 0.5,
         verbose      = TRUE,
         niter        = 10,
         cvg.check    = NULL,
         describe.preprocessing = TRUE,
         pstart,
         strata)

_A_r_g_u_m_e_n_t_s:

intensities: An object that contains intensity values from a microarray
          experiment. See 'getIntensityMatrix' for details. The
          intensities are assumed to be the raw scanner data,
          summarized over the spots by an image analysis program, and
          possibly "background subtracted". The intensities must not be
          logarithmically or otherwise transformed, and not thresholded
          or "floored". NAs are not accepted. See details.

lts.quantile: Numeric. The quantile that is used for the resistant
          least trimmed sum of squares regression. Allowed values are
          between 0.5 and 1. A value of 1 corresponds to ordinary least
          sum of squares regression.

 verbose: Logical. If TRUE, some messages are printed.

   niter: Integer. The number of iterations to be used in the least
          trimmed sum of squares regression.

cvg.check: List. If non-NULL, this allows finer control of the
          iterative least trimmed sum of squares regression. See
          details.

  pstart: Array. If not missing, user can specify start values for the
          iterative parameter estimation algorithm. See  'vsnh' for
          details.

describe.preprocessing: Logical. If TRUE, calibration and
          transformation parameters, plus some other information are
          stored in the 'preprocessing' slot of the returned object.
          See details.

  strata: Integer vector. Its length must be the same as
          nrow(intensities). This parameter allows for the calibration
          and error model parameters to be stratified within each
          array, e.g to take into account probe  sequence properties,
          print-tip or plate effects.   If 'strata' is not specified,
          one pair of parameters is fitted for every sample (i.e. for
          every column of 'intensities'). If 'strata' is specified, a
          pair of parameters is fitted for every  stratum within every
          sample. The strata are coded for by the different integer
          values. The integer vector 'strata' can be obtained from a
          factor 'fac' through 'as.integer(fac)', from a character
          vector 'str' through 'as.integer(factor(fac))'.

_D_e_t_a_i_l_s:

     *Overview:*  The function calibrates for sample-to-sample
     variations through shifting and scaling, and transforms the
     intensities to a scale where the variance is approximately
     independent of the mean intensity. The variance stabilizing
     transformation is equivalent to the natural logarithm in the
     high-intensity range, and to a linear transformation in the
     low-intensity range. In an intermediate range, the _arsinh_
     function interpolates smoothly between the two. For details on the
     transformation, please see the help page for 'vsnh'. The
     parameters are estimated through a robust variant of maximum
     likelihood. This assumes that for the majority of genes the
     expression levels are not much different across the samples, i.e.,
     that only a minority of genes (less than a fraction
     '1-lts.quantile') is differentially expressed.

     Even if most genes on an array are differentially expressed, it
     may still be possible to use the estimator: if a set of
     non-differentially expressed genes is known, e.g. because they are
     external controls or reliable 'house-keeping genes', the
     transformation parameters can be fitted with 'vsn' from the data
     of these genes, then the transformation can be applied to all data
     with 'vsnh'.

     *Format:* The format of the matrix of intensities is as follows:
     for the *two-color printed array technology*, each row corresponds
     to one spot, and the columns to the different arrays and
     wave-lengths (usually red and green, but could be any number). For
     example, if there are 10 arrays, the matrix would have 20 columns,
     columns 1...10 containing the green intensities, and 11...20 the
     red ones. In fact, the ordering of the columns does not matter to
     'vsn', but it is your responsibility to keep track of it for
     subsequent analyses. For *one-color arrays*, each row corresponds
     to a probe, and each column to an array.

     *Performance:* This function is slow. That is due to the nested
     iteration loops of the numerical optimization of the likelihood
     function and the heuristic that identifies the non-outlying data
     points in the least trimmed squares regression. For large arrays
     with many tens of thousands of probes, you may want to consider
     random subsetting: that is, only use a subset of the e.g.
     10-20,000 rows of the data matrix 'intensities' to fit the
     parameters, then apply the transformation to all the data, using
     'vsnh'. An example for this can be seen in the function
     'normalize.AffyBatch.vsn', whose code you can inspect by typing
     'normalize.AffyBatch.vsn' on the R command line.

     *Iteration control:*  By default, if 'cvg.check' is 'NULL', the
     function will run the fixed number 'niter' of iterations in the
     least trimmed sum of squares regression. More fine-grained control
     can be obtained by passing a list with elements 'eps' and 'n'. If
     the maximum change between transformed data values is smaller than
     'eps' for 'n' subsequent iterations, then the iteration
     terminates.

     *Estimated transformation parameters:*  If
     'describe.preprocessing' is 'TRUE', the transformation parameters
     are returned in the 'preprocessing' slot of the 'description' slot
     of the resulting  'exprSet' object, in the form  of a 'list' with
     three elements

        *  'vsnParams': the parameter array (see 'vsnh'  for details) 

        *  'vsnParamsIter': an array with dimensions  'c(dim(vsnParams,
           niter))' that contains the parameter  trajectory during the
           iterative fit process (see also  'vsnPlotPar').

        *  'vsnTrimSelection': a logical vector that for each row of
           the intensities matrix reports whether it was below (TRUE)
           or above (FALSE) the trimming threshold.

     If 'intensities' has class 'exprSet', and its 'description' slot
     has class 'MIAME', then this list is appended to any existing
     entries in the 'preprocessing' slot. Otherwise, the 'description'
     object and its 'preprocessing' slot are created.

_V_a_l_u_e:

     An object of class 'exprSet'. Differences between the columns of
     the transformed intensities may be interpreted as "generalized
     log-ratios". For the transformation parameters, please see
     details.

_A_u_t_h_o_r(_s):

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

_R_e_f_e_r_e_n_c_e_s:

     Variance stabilization applied to microarray data calibration and
     to the quantification of differential expression, Wolfgang Huber,
     Anja von Heydebreck, Holger Sueltmann, Annemarie Poustka, Martin
     Vingron; Bioinformatics (2002) 18 Suppl.1 S96-S104.

     Parameter estimation for the calibration and variance
     stabilization  of microarray data,  Wolfgang Huber, Anja von
     Heydebreck, Holger Sueltmann,  Annemarie Poustka, and Martin
     Vingron;   Statistical Applications in Genetics and Molecular
     Biology (2003) Vol. 2 No. 1, Article 3.
     http://www.bepress.com/sagmb/vol2/iss1/art3.

_S_e_e _A_l_s_o:

     'vsnh', 'vsnPlotPar',  'exprSet-class',  'MIAME-class',
     'normalize.AffyBatch.vsn'

_E_x_a_m_p_l_e_s:

     data(kidney)
     log.na = function(x) log(ifelse(x>0, x, NA))

     if(interactive()) {
       x11(width=9, height=4.5)
       par(mfrow=c(1,2))
     }
     plot(log.na(exprs(kidney)), pch=".", main="log-log")

     vsnkid = vsn(kidney)   ## transform and calibrate
     plot(exprs(vsnkid), pch=".", main="h-h")

     if (interactive()) {
       x11(width=9, height=4)
       par(mfrow=c(1,3))
     }

     meanSdPlot(vsnkid)
     vsnPlotPar(vsnkid, "factors")
     vsnPlotPar(vsnkid, "offsets")

     ## this should always hold true
     params = preproc(description(vsnkid))$vsnParams
     stopifnot(all(vsnh(exprs(kidney), params) == exprs(vsnkid))) 

