gls.series               package:limma               R Documentation

_F_i_t _L_i_n_e_a_r _M_o_d_e_l _t_o _M_i_c_r_o_a_r_r_a_y _D_a_t_a _b_y _G_e_n_e_r_a_l_i_z_e_d _L_e_a_s_t _S_q_u_a_r_e_s

_D_e_s_c_r_i_p_t_i_o_n:

     Fit a linear model genewise to expression data from a series of
     microarrays. The fit is by generalized least squares allowing for
     correlation between duplicate spots or related arrays. This is a
     utility function for 'lmFit'.

_U_s_a_g_e:

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

_A_r_g_u_m_e_n_t_s:

       M: numeric matrix containing log-ratio or log-expression values
          for a series of microarrays, rows correspond to genes and
          columns to arrays.

  design: numeric design matrix defining the linear model, 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: 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

   block: vector or factor specifying a blocking variable on the
          arrays. Same length as 'ncol(M)'.

correlation: numeric value specifying the inter-duplicate or
          inter-block 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:

     This is a utility function used by the higher level function
     'lmFit'. Most users should not use this function directly but
     should use 'lmFit' instead.

     This function is for fitting gene-wise linear models when some of
     the expression values are correlated. The correlated groups may
     arise from replicate spots on the same array (duplicate spots) or
     from a biological or technical replicate grouping of the arrays.
     This function is normally called by 'lmFit' and is not normally
     called directly by users.

     Note that the correlation is assumed to be constant across genes.
     If 'correlation=NULL' then a call is made to
     'duplicateCorrelation' to estimated the correlation.

_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 or inter-block correlation

      qr: QR decomposition of the generalized linear squares problem,
          i.e., the decomposition of 'design' standardized by the
          Choleski-root of the correlation matrix defined by
          'correlation'

_A_u_t_h_o_r(_s):

     Gordon Smyth

_S_e_e _A_l_s_o:

     'duplicateCorrelation'.

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