gls.series               package:limma               R Documentation

_G_e_n_e_r_a_l_i_z_e_d _L_e_a_s_t _S_q_u_a_r_e_s _f_o_r _S_e_r_i_e_s _o_f _M_i_c_r_o_a_r_r_a_y_s

_D_e_s_c_r_i_p_t_i_o_n:

     Fit linear models for each gene to a series of microarrays. Fit is
     by generalized least squares allowing for correlation between
     duplicate spots.

_U_s_a_g_e:

     gls.series(M,design=rep(1,ncol(M)),ndups=2,spacing=1,correlation=NULL,weights=NULL,...)

_A_r_g_u_m_e_n_t_s:

       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'.

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

correlation: the inter-duplicate correlation.

 weights: an optional numeric matrix of the same dimension as 'M'
          containing weights for each spot. If it is of different
          dimension to 'M', it will be filled out to the same size.

     ...: other optional arguments to be passed to 'dupcor.series'.

_D_e_t_a_i_l_s:

     Normally 'dupcor.series' will be called before 'gls.series' to
     estimate the inter-duplicate correlation. Then t-statistics will
     be formed to rank the genes in order of evidence for differential
     expression.

_V_a_l_u_e:

     A list with components 

coefficients: numeric matrix containing the estimated coefficients for
          each linear model. Same number of rows as 'M', same number of
          columns as 'design'.

stdev.unscaled: numeric matrix conformal with 'coef' containing the
          unscaled standard deviations for the coefficient estimators.
          The standard errors are given by 'stdev.unscaled * sigma'.

   sigma: numeric vector containing the residual standard deviation for
          each gene.

df.residual: numeric vector giving the degrees of freedom corresponding
          to 'sigma'.

correlation: inter-duplicate correlation.

_A_u_t_h_o_r(_s):

     Gordon Smyth

_S_e_e _A_l_s_o:

     'dupcor.series'.

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

_E_x_a_m_p_l_e_s:

     M <- matrix(rnorm(10*6),10,6)
     cor.out <- dupcor.series(M)
     #  See cor.out$cor for estimated common correlation
     fit <- gls.series(M,correlation=cor.out$cor)

