lmscFit                package:limma                R Documentation

_F_i_t _L_i_n_e_a_r _M_o_d_e_l _t_o _I_n_d_i_v_i_d_u_a_l _C_h_a_n_n_e_l_s _o_f _T_w_o-_C_o_l_o_r _D_a_t_a

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

     Fit a linear model to the individual log-intensities for each gene
     given a series of two-color arrays

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

     lmscFit(object, design, correlation)

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

  object: an 'MAList' object or a list from which 'M' and 'A' values
          may be extracted

  design: a numeric matrix containing the design matrix for linear
          model in terms of the individual channels. The number of rows
          should be twice the number of arrays. The number of columns
          will determine the number of coefficients estimated for each
          gene.

correlation: numeric value giving the intra-spot correlation

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

     For two color arrays, the channels measured on the same set of
     arrays are correlated. The 'M' and 'A' however are uncorrelated
     for each gene. This function fits a linear model to the set of M
     and A-values for each gene after re-scaling the M and A-values to
     have equal variances. The input correlation determines the scaling
     required. The input correlation is usually estimated using
     'intraspotCorrelation' before using 'lmscFit'.

     Missing values in 'M' or 'A' are not allowed.

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

     An object of class 'MArrayLM'

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

     Gordon Smyth

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

     Smyth, G. K. (2005). Individual channel analysis of two-colour
     microarray data. _Proceedings of the 55th Session of the
     International Statistics Institute_, 5-12 April 2005, Sydney,
     Australia, Paper 116. <URL:
     http://www.statsci.org/smyth/pubs/ISI2005-116.pdf>

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

     'lm.fit'.

     An overview of methods for single channel analysis in limma is
     given by 07.SingleChannel.

_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="normexp")
     MA <- normalizeWithinArrays(RG,mouse.setup)
     MA <- normalizeBetweenArrays(MA,method="Aq")
     # Randomly choose 500 genes for this example
     i <- sample(1:nrow(MA),500)
     MA <- MA[i,]
     targets <- data.frame(Cy3=I(rep("Pool",6)),Cy5=I(c("WT","WT","WT","KO","KO","KO")))
     targets.sc <- targetsA2C(targets)
     targets.sc$Target <- factor(targets.sc$Target,levels=c("Pool","WT","KO"))
     design <- model.matrix(~Target,data=targets.sc)
     corfit <- intraspotCorrelation(MA,design)
     fit <- lmscFit(MA,design,correlation=corfit$consensus)
     cont.matrix <- cbind(KOvsWT=c(0,-1,1))
     fit2 <- contrasts.fit(fit,cont.matrix)
     fit2 <- eBayes(fit2)
     topTable(fit2,adjust="fdr")

