dupcor.series             package:limma             R Documentation

_C_o_r_r_e_l_a_t_i_o_n _B_e_t_w_e_e_n _D_u_p_l_i_c_a_t_e_s

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

     Estimate the correlation between duplicate spots (regularly spaced
     replicate spots on the same array) or between technical replicates
     from a series of arrays.

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

     duplicateCorrelation(object,design=rep(1,ncol(M)),ndups=2,spacing=1,block=NULL,trim=0.15,weights=NULL)
     dupcor.series(M,design=rep(1,ncol(M)),ndups=2,spacing=1,initial=0.8,trim=0.15,weights=NULL)

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

  object: a numeric matrix of log-ratios or an 'MAList' object from
          which the log-ratios can be extracted. If 'object' is an
          'MAList' then the arguments 'design', 'ndups', 'spacing' and
          'weights' will be extracted from it if available and do not
          have to be specified as arguments.

       M: a numeric matrix. Usually the log-ratios of expression for a
          series of cDNA microarrrays with rows corresponding to genes
          and columns to arrays.

  design: the design matrix of the microarray experiment, with rows
          corresponding to arrays and columns to comparisons to be
          estimated. The number of rows must match the number of
          columns of 'M'. Defaults to the unit vector meaning that the
          arrays are treated as replicates.

   ndups: a positive integer giving the number of times each gene is
          printed on an array. 'nrow(M)' must be divisible by 'ndups'.
          Will be ignored if 'block' is specified.

 spacing: the spacing between the rows of 'M' corresponding to
          duplicate spots, 'spacing=1' for consecutive spots

   block: vector or factor specifying a blocking variable

 initial: a numeric value between -1 and 1 giving an initial estimate
          for the correlation. Not currently used.

    trim: the fraction of observations to be trimmed from each end of
          'tanh(all.correlations)' when computing the trimmed mean.

 weights: an optional numeric matrix of the same dimension as 'M'
          containing weights for each spot. If smaller than 'M' then it
          will be filled out the same size.

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

     When 'block=NULL', this function estimates the correlation between
     duplicate spots (regularly spaced within-array replicate spots).
     If 'block' is not null, this function estimates the correlation
     between repeated observations on the blocking variable. Typically
     the blocks are biological replicates and the repeated observations
     are technical replicates. In either case, the correlation is
     estimated by fitting a mixed linear model by REML individually for
     each gene. The function also returns a consensus correlation,
     which is a robust average of the individual correlations, which
     can be used as input for  functions 'lmFit' or 'gls.series'.

     At this time it is not possible to estimate correlations between
     duplicate spots and between technical replicates simultaneously.
     If 'block' is not null, then the function will set 'ndups=1'.

     The function may take long time to execute as it fits a mixed
     linear model for each gene.

     'dupcor.series' produces the same values as 'duplicateCorrelation'
     and it retained for compatibility with earlier releases of the
     software.

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

     A list with components 

consensus.correlation: the average estimated inter-duplicate
          correlation. The average is the 0.1 trimmed mean of the
          correlations for individual genes on the tanh-transformed
          scale.

     cor: same as 'consensus.correlation', for compatibility with
          earlier versions of the software

all.correlations: a numeric vector of length 'nrow(M)/ndups' giving the
          individual genewise correlations.

_A_u_t_h_o_r(_s):

     Gordon Smyth

_R_e_f_e_r_e_n_c_e_s:

     Smyth, G. K., Michaud, J., and Scott, H. (2003). The use of
     within-array duplicate spots for assessing differential expression
     in microarray experiments. <URL:
     http://www.statsci.org/smyth/pubs/dupcor.pdf>

_S_e_e _A_l_s_o:

     These functions use 'randomizedBlockFit' from the statmod package.

     An overview of linear model functions in limma is given by
     5.LinearModels.

_E_x_a_m_p_l_e_s:

     #  See gls.series for an example

