arrayWeights              package:limma              R Documentation

_A_r_r_a_y _Q_u_a_l_i_t_y _W_e_i_g_h_t_s

_D_e_s_c_r_i_p_t_i_o_n:

     Estimates relative quality weights for each array in a multi-array
     experiment.

_U_s_a_g_e:

     arrayWeights(object, design = NULL, weights = NULL, method = "genebygene", maxiter = 50, tol = 1e-10, trace=FALSE)
     arrayWeightsSimple(object, design = NULL, maxiter = 100, tol = 1e-6, maxratio = 100, trace=FALSE)

_A_r_g_u_m_e_n_t_s:

  object: object of class 'numeric', 'matrix', 'MAList', 'marrayNorm',
          'ExpressionSet' or 'PLMset' containing log-ratios or
          log-values of expression for a series of microarrays.

  design: the design matrix of the microarray experiment, with rows
          corresponding to arrays and columns to coefficients to be
          estimated.  Defaults to the unit vector meaning that the
          arrays are treated as replicates.

 weights: optional numeric matrix containing prior weights for each
          spot.

  method: character string specifying the estimating algorithm to be
          used. Choices are '"genebygene"' and '"reml"'.

 maxiter: maximum number of iterations allowed.

     tol: convergence tolerance.

maxratio: maximum ratio between largest and smallest weights before
          iteration stops

   trace: logical variable. If true then output diagnostic information
          at each iteration of '"reml"' algorithm.

_D_e_t_a_i_l_s:

     The relative reliability of each array is estimated by measuring
     how well the expression values for that array follow the linear
     model.

     The method is described in Ritchie et al (2006). A heteroscedastic
     model is fitted to the expression values for  each gene by calling
     the function 'lm.wfit'.  The dispersion model  is fitted to the
     squared residuals from the mean fit, and is set up to  have array
     specific coefficients, which are updated in either full REML 
     scoring iterations, or using an efficient gene-by-gene update
     algorithm.   The final estimates of these array variances are
     converted to weights.

     The data object 'object' is interpreted as for 'lmFit'. In
     particular, the arguments 'design' and 'weights' will be extracted
     from the data  'object' if available and do not normally need to
     be set explicitly in  the call; if any of these are set in the
     call then they will over-ride  the slots or components in the data
     'object'.

     'arrayWeightsSimple' is a fast version of 'arrayWeights' with
     'method="reml"', no prior weights and no missing values.

_V_a_l_u_e:

     A vector of array weights.

_A_u_t_h_o_r(_s):

     Matthew Ritchie and Gordon Smyth

_R_e_f_e_r_e_n_c_e_s:

     Ritchie, M. E., Diyagama, D., Neilson, van Laar, R., J., Dobrovic,
     A., Holloway, A., and Smyth, G. K. (2006). Empirical array quality
     weights in the analysis of microarray data. BMC Bioinformatics 7,
     261. <URL: http://www.biomedcentral.com/1471-2105/7/261/abstract>

_S_e_e _A_l_s_o:

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

_E_x_a_m_p_l_e_s:

     library(sma)
     # Subset of data from ApoAI case study in Limma User's Guide
     data(MouseArray)
     # Avoid non-positive intensities
     RG <- backgroundCorrect(mouse.data, method="half")
     MA <- normalizeWithinArrays(RG, mouse.setup)
     MA <- normalizeBetweenArrays(MA, method="Aq")
     targets <- data.frame(Cy3=I(rep("Pool",6)),Cy5=I(c("WT","WT","WT","KO","KO","KO")))
     design <- modelMatrix(targets, ref="Pool")
     arrayw <- arrayWeightsSimple(MA, design)
     fit <- lmFit(MA, design, weights=arrayw)
     fit2 <- contrasts.fit(fit, contrasts=c(-1,1))
     fit2 <- eBayes(fit2)
     # Use of array weights increases the significance of the top genes
     topTable(fit2)

