rlm.series               package:limma               R Documentation

_R_o_b_u_s_t _L_i_n_e_a_r _M_o_d_e_l _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 robust M-estimation.

_U_s_a_g_e:

     rlm.series(M,design=NULL,ndups=1,spacing=1,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

 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.

     ...: any other arguments are passed to 'rlm.default'.

_D_e_t_a_i_l_s:

     The linear model is fit for each gene by calling the function
     'rlm' from the MASS library.

     Warning: don't use weights with this function unless you
     understand how 'rlm' treats weights. The treatment of weights is
     different from that of 'lm.series' and 'gls.series'.

_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'.

_A_u_t_h_o_r(_s):

     Gordon Smyth

_S_e_e _A_l_s_o:

     'rlm'.

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

_E_x_a_m_p_l_e_s:

     #  Simulate gene expression data,
     #  6 microarrays and 100 genes with one gene differentially expressed in first 3 arrays
     M <- matrix(rnorm(100*6,sd=0.3),100,6)
     M[1,1:3] <- M[1,1:3] + 2
     #  Design matrix includes two treatments, one for first 3 and one for last 3 arrays
     design <- cbind(First3Arrays=c(1,1,1,0,0,0),Last3Arrays=c(0,0,0,1,1,1))
     fit <- rlm.series(M,design=design)
     eb <- ebayes(fit)
     #  Large values of eb$t indicate differential expression
     qqt(eb$t[,1],df=fit$df+eb$df.prior)
     abline(0,1)

